Today's video games may boast photorealistic graphics, surround sound and worldwide multiplayer support, but many still long for the days when games were simple. You know, when a game didn't require more than a joystick and a button or two? Perhaps it's no surprise, many are buying cabinets for the home, including replicas of classic coin-operated ("coin-op") games and pinball machines. "Simple games that are'quick to learn but difficult to master' have a special addictive quality that we tried for when designing them with our limited graphic palette," recalls Nolan Bushnell, who established Atari and Pong in the '70s, and shortly thereafter, founded Chuck E. Cheese (smartly, as a distribution channel for Atari games). "Often games are for turning off your mind and entering kind of a Zen state."

Developers have taught artificial intelligence how to play an arcade pinball machine, which learned so quickly it could beat human players within four days. Speaking at Microsoft's developer conference, Build, which is being held virtually this week, Jack Skinner described how he and a team of developers in Sydney used artificial intelligence to control an actual pinball machine. The team took a regular arcade machine and adapted it, using a Windows computer to control the AI software, and a Raspberry Pi to control the flipper mechanism within the pinball machine. Two webcams were mounted on the pinball machine - one pointed at the scoreboard and one pointed down at the table - so that the AI could "see" the table like a human player would. Optical character recognition (OCR) software allowed the computer to read the current score from the pinball machine's electronic display.

How are we making computers do the things we used to associated only with humans? Have we made a breakthrough in understanding human intelligence? While recent achievements might give the sense that the answer is yes, the short answer is that we are nowhere near. All we've achieved for the moment is a breakthrough in emulating intelligence. A more recent example comes from the middle of the last century. A hundred years ago computers were human beings, often female, who conducted repetitive mathematical tasks for the creation of mathematical tables such as logarithms. Our modern digital computers were originally called automatic computers to reflect the fact that the intelligence of these human operators had been automated. But despite the efficiency with which they perform these tasks, very few think of their mobile phones or computers as intelligent. Norbert Wiener launched last century's first wave of interest in emulation of intelligence with his book "Cybernetics".

Al Alcorn knew he was being wooed. Nolan Bushnell, the tall, brash, young engineer from Alcorn's work-study days at Ampex, had shown up at Alcorn's Sunnyvale office. Bushnell was driving a new blue station wagon. "It's a company car," he said with feigned nonchalance. He offered to drive Alcorn, recently hired as an associate engineer at Ampex, to see the "game on a TV screen" that Bushnell and Ted Dabney had developed at their new startup company. The two men drove to an office in Mountain View, near the highway. The space was large, about 10,000 square feet, and looked like a cross between an electronics lab and an assembly warehouse. Oscilloscopes and lab benches filled one area. Half-built cabinets and screen with wires protruding from them sat in another. Bushnell walked with Alcorn to a sinuous, six-foot-tall fiberglass cabinet with a screen at eye level. Bushnell was proud of what he called its "spacey-looking" shape.

Al Alcorn knew he was being wooed. Nolan Bushnell, the tall, brash, young engineer from Alcorn's work-study days at Ampex, had shown up at Alcorn's Sunnyvale office. Bushnell was driving a new blue station wagon. "It's a company car," he said with feigned nonchalance. He offered to drive Alcorn, recently hired as an associate engineer at Ampex, to see the "game on a TV screen" that Bushnell and Ted Dabney had developed at their new startup company. The two men drove to an office in Mountain View, near the highway. The space was large, about 10,000 square feet, and looked like a cross between an electronics lab and an assembly warehouse. Oscilloscopes and lab benches filled one area. Half-built cabinets and screen with wires protruding from them sat in another. Bushnell walked with Alcorn to a sinuous, six-foot-tall fiberglass cabinet with a screen at eye level. Bushnell was proud of what he called its "spacey-looking" shape.

The Department of Computer Science at the University of Southern California recently created two new degree programs, namely a Bachelor's Program in Computer Science (Games) and a Master's Program in Computer Science (Game Development). In this paper, we discuss two projects that use games as motivator. First, the Computer Games in the Classroom Project develops stand-alone projects on standard artificial intelligence topics that use video-game technology to motivate the students but do not require the students to use game engines. Second, the Pinball Project develops the necessary hardware and software to enable students to learn concepts from robotics by developing games on actual pinball machines.

Roboticists need to have a solid understanding of hardware and software. The standard computer science education in the United States, however, tends to teach students only about software. To remedy this situation, we explore new ways of teaching them about hardware in a playful way. Realizing that pinball machines are simple robots, we have developed a pinball machine interface between a PC and a recent Lord of the Rings pinball machine, which enables students to implement pinball games and gain knowledge of hardware and interface programming in the process. This paper describes both our pinball machine interface and our experience developing it. As far as we know, this is the first time that anyone has managed to control an existing pinball machine completely.

Compared to invasive Brain-Computer Interfaces (BCI), non-invasive BCI systems based on Electroencephalogram (EEG) signals have not been applied successfully for complex control tasks. In the present study, however, we demonstrate this is possible and report on the interaction of a human subject with a complex real device: a pinball machine. First results in this single subject study clearly show that fast and well-timed control well beyond chance level is possible, even though the environment is extremely rich and requires complex predictive behavior. Using machine learning methods for mental state decoding, BCI-based pinball control is possible within the first session without the necessity to employ lengthy subject training. While the current study is still of anecdotal nature, it clearly shows that very compelling control with excellent timing and dynamics is possible for a non-invasive BCI.