The acceleration of technological progress has been the central feature of this century. We are on the edge of change comparable to the rise of human life on Earth. The precise cause of this change is the imminent creation by technology of entities with greater-than-human intelligence. Science may achieve this breakthrough by several means (and this is another reason for having confidence that the event will occur): Computers that are "awake" and superhumanly intelligent may be developed. (To date, there has been much controversy as to whether we can create human equivalence in a machine. But if the answer is "yes," then there is little doubt that more intelligent beings can be constructed shortly thereafter.); Large computer networks (and their associated users) may "wake up" as superhumanly intelligent entities; Computer/human interfaces may become so intimate that users may reasonably be considered superhumanly intelligent; Biological science may provide means to improve natural human intellect.

The United States has experienced a spate of disasters in the past three years, from 9/11 to the Quecreek Mine collapse to the current hurricane season in Florida. Disasters are powerful reminders of our fragile, temporary existence on this earth, but they are also object lessons in what could be done with robots that will transcend our individual contributions. This talk will discuss the use of robots and related technologies for emergency response, review the lessons learned from the field, and pose a list of research challenges. Video from the World Trade Center, Hurricane Charley, and numerous field exercises from the Center for Robot-Assisted Search and Rescue will be shown.

Our objective in computational neuroscience is to elucidate information processing of the brain to the extent that artificial machines, either computer programs or robots, can be built to solve the same computational problems that are solved by the brain, essentially in the same principle. Our humanoid robot DB has been the central test bed of our various computational theories (cerebellar internal models, imitation learning, reinforcement learning, etc), and can exhibit about 30 different tasks. I introduce the concept of non-invasive, high-performance brain machine interface based on hierarchical Bayesian estimation of brain activity from MEG/EEG data, that may be able to connect human brains and humanoid robots.

Twenty five years ago the Leg Laboratory set out to create a family of dynamic legged robots -- robots that would run, jump, balance actively and do gymnastic maneuvers. Last year we revived the LegLab with the goal of creating a new generation of robots that will travel on rough terrain, terrain too steep, rugged and varied for wheeled or tracked vehicles. In this talk we will take stock, asking what parts of the legged robot problem have changed in the last 25 years, and what parts remain the same?

Vision is one of the first areas that Artificial Intelligence tackled. However, the earlier "Let's-program-what-I-think-I-am-doing" approach was not as successful. Today, the two fields, Computer Vision and Artificial Intelligence, have very little interaction, and the goal of developing a general vision system, such as understanding natural scenes, continues to be least understood or is almost abandoned. This talk will start with my historical perspectives on how this happened, and then present the argument that there is an opportunity to renew the tie between the two fields for the purpose of developing a capable AI-based vision system as a gigantic search problem.

The paradigm shift rate is now doubling every decade, so the twenty-first century will see 20,000 years of progress at today's rate. Computation, communication, biological technologies (for example, DNA sequencing), brain scanning, knowledge of the human brain, and human knowledge in general are all accelerating at an even faster pace, generally doubling price-performance, capacity, and bandwidth every year. The well-known Moore's Law is only one example of many of this inherent acceleration. The size of the key features of technology is also shrinking, at a rate of about 4 per linear dimension per decade. Three-dimensional molecular computing will provide the hardware for human-level "strong" AI well before 2030. The more important software insights will be gained in part from the reverse-engineering of the human brain, a process well under way.

The fraction of value of products and services comprised by information is rapidly asymptoting to 100 percent The deflation rate for information technologies, both hardware and software, is about 50 percent per year, providing a powerful deflationary force in the economy. Despite this, the information technology industry grows around 18 percent per year, now comprises 8 percent of the GDP, and is deeply influential on the rest. Within a couple of decades, the bulk of the economy will be dominated by information and software.

Once nonbiological intelligence matches the range and subtlety of human intelligence, it will necessarily soar past it because of the continuing acceleration of information-based technologies, as well as the ability of machines to instantly share their knowledge. Intelligent nanorobots will be deeply integrated in the environment, our bodies and our brains, providing vastly extended longevity, full-immersion virtual reality incorporating all of the senses, experience "beaming," and enhanced human intelligence. The implication will be an intimate merger between the technology-creating species and the evolutionary process it spawned.