If Algirdas Budrys–who signed his work "Algis Budrys" and answered to "Ajay" among the regular Americans with whom he lived–maintained an apprehensive watchfulness toward much of the human race, it wasn't without justification. To start with, as the small son of Lithuania's consul general in Königsberg, East Prussia, he had seen Adolf Hitler pass in full Nazi pomp, while the citizens of the city where Immanuel Kant lay buried whipped themselves into such frenzies of admiration that they soiled themselves and defecated in public. More than seven decades later, dying in a Chicago suburb, Budrys still remembered what he had seen from the second-story window of his parents' apartment on that spring day in 1936. He told me, "After the Hitlerjugend walked through, Hitler came by in an open black Mercedes with his arm propped up. I'm sure he had an iron bar up his sleeve, because he couldn't have kept his arm that particular way for so long otherwise."

We recently ran across Alelo (www.alelo.com), The company's very engaging, interactive 3D role playing games teach languages like Arabic and Pashto to troops being deployed to the Middle East. Using speech recognition and other technology, the titles teach foreign languages to players as they go through the game in simulated environments like Iraq. We spoke with Dr. W. Lewis Johnson, CEO of Alelo, about the firm's technology and plans. Ben Kuo: Tell us a little bit about Alelo, and what the company and product does?

Quantum information science is a young field, its underpinnings still being laid by a large number of researchers [see "Rules for a Complex Quantum World," by Michael A. Nielsen; Scientific American, November 2002]. Classical information science, by contrast, sprang forth about 50 years ago, from the work of one remarkable man: Claude E. Shannon. In a landmark paper written at Bell Labs in 1948, Shannon defined in mathematical terms what information is and how it can be transmitted in the face of noise. What had been viewed as quite distinct modes of communication--the telegraph, telephone, radio and television--were unified in a single framework. Shannon was born in 1916 in Petoskey, Michigan, the son of a judge and a teacher.

Henry E. Kyburg Jr., a renowned and respected professor of philosophy and computer science at the University of Rochester, died of acute pancreatitis Oct. 30 at the age of 79 at Strong Memorial Hospital. He was well-known for his cutting-edge studies of uncertain inference, which is the human process of reaching conclusions, and data mining, the process by which computers search for information in data or draw conclusions from it. Kyburg, Burbank Professor of Intellectual and Moral Philosophy, was honored in 2007 with the University Award for Lifetime Achievement in Graduate Education. He was clearly admired by his students--who can be found working as pioneers themselves across all disciplines at research and educational institutions--for his insightful instruction, generous spirit, and relentless energy. "The last thing he said to me was'I would like a logic problem to work on,' because Henry was always scribbling, loved his work, and in general never stayed idle," said his wife Sarah Kyburg, who lived with her husband and eight children on their sustainable farm in Lyons, N.Y.

IBM computer scientists have been at the forefront of scientific and technological innovation in a broad range of research areas. They have made pioneering contributions in artificial intelligence, high-speed processor design, computer architecture, natural language processing, programming languages, optimizing compilers, operating systems, storage systems, computer-supported cooperative work, databases, speech recognition, integer programming and service-oriented architectures, to name a few. Audio podcast features short presentations on subjects ranging from micropayments to Ebola research. IBM researchers from worldwide labs summarize innovations in big data, cloud analytics, cognitive science and many other topics in computer science, electrical engineering and mathematical sciences. Recipients of annual awards, sponsored by Professional Interest Communities (PIC), for papers high in technical significance and impact on computer science, electrical engineering and mathematical sciences.

The Turing award, in existence since 1966, comes with a $250,000 prize funded by Google and Intel. Last year's award went to Leslie Valiant, a Harvard University computer scientist. One past winner, Internet pioneer Vinton Cerf, says Pearl's accomplishments have "redefined the term'thinking machine'" over the past 30 years. Pearl's efforts have had "a pervasive influence not only on machine learning but on natural language processing, computer vision, robotics, computational biology, econometrics, cognitive science and statistics," Cerf said in a statement. The UCLA computer science professor is widely credited with coining the term "Bayesian Network," which refers to a statistical model ACM describes as mimicking "the neural activities of the human brain, constantly exchanging messages without benefit of a supervisor."

Marvin Minsky has long been one of the great human intelligences working in the field of artificial intelligence (AI). A professor at MIT, where he has worked since 1957 and cofounded the AI laboratory in 1959, Minsky is also an inventor, philosopher, and author. In recent years, Minsky has focused his formidable talents on trying to impart the human capacity for commonsense reasoning to machines. In this interview, conducted on November 3, 2010 by "Smartest Machine on Earth" producer Michael Bicks, hear Minsky's take on why it's important to recreate human intelligence, what a five-year-old can do that even the smartest machine cannot, and whether someone will ever invent a computer that laughs at Seinfeld. Marvin Minsky says that when it comes to designing a smart machine, "you mustn't look for a magic bullet"--that is, just a single way to solve all problems.

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.

David E. Rumelhart, whose computer simulations of perception gave scientists some of the first testable models of neural processing and proved helpful in the development of machine learning and artificial intelligence, died Sunday in Chelsea, Mich. The cause was complications of Pick's disease, an Alzheimer's-like disorder from which he had suffered for more than a decade, his son Karl said. When Dr. Rumelhart, a psychologist, began thinking in the 1960s about how neurons process information, the field was split into two camps that had little common language: biologists, who focused on neurons and brain tissue; and cognitive psychologists, who studied far more abstract processes, like reasoning skills and learning strategies. By starting small -- showing, for instance, that the brain's ability to recognize a single letter was greatly influenced by the letters around it -- Dr. Rumelhart and his colleague Jay McClelland, around 1980, built computer programs that roughly simulated perception. Later, he devised an algorithm that allowed computer programs to learn how to perceive.

Oliver G. Selfridge, an innovator in early computer science and artificial intelligence, died on Wednesday in Boston. The cause was injuries suffered in a fall on Sunday at his home in nearby Belmont, Mass., said his companion, Edwina L. Rissland. Credited with coining the term "intelligent agents," for software programs capable of observing and responding to changes in their environment, Mr. Selfridge theorized about far more, including devices that would not only automate certain tasks but also learn through practice how to perform them better, faster and more cheaply. Eventually, he said, machines would be able to analyze operator instructions to discern not just what users requested but what they actually wanted to occur, not always the same thing. His 1958 paper "Pandemonium: A Paradigm for Learning," which proposed a collection of small components dubbed "demons" that together would allow machines to recognize patterns, was a landmark contribution to the emerging science of machine learning.