If you have seen the movie'Iron Man', you might remember Tony Stark's home computing system J.A.R.V.I.S; an artificially intelligent home computing system that took care of everything from the home's heating and cooling systems to Stark's hot rod in the garage. It looked pretty great on screen, but unlike to AI technologies today, Stark's AI assistant:
How can we create robots that can carry out important tasks in dangerous environments? Machine learning is supporting advances in the field of robotics. To find out more, we talked to Dr Rustam Stolkin, Royal Society Industry Fellow for Nuclear Robotics, Professor of Robotics at the University of Birmingham, and Director at A.R.M Robotics Ltd, about his work combining machine learning and robotics to create practical solutions to nuclear problems.
Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of "intelligent agents" – any device that perceives its environment and takes actions that maximize its chance of success at some goal. Artificial general intelligence (AGI) is the intelligence of a machine that could successfully perform any intellectual task that a human being can. It is a primary goal of some artificial intelligence research and a common topic in science fiction and futurism. Machine learning is a type of AI that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data. Deep learning is a specific machine learning technique. Most deep learning methods involve artificial neural networks, modeling how our brain works. At the moment deep learning forms the basis for most of the incredible advances in machine learning (and in turn AI). Big data is a term for extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.
MIT research scientist Richard Fletcher directs the Mobile Technology Group at MIT D-Lab, which develops a variety of mobile sensors, analytic tools, and diagnostic algorithms to study problems in global health and behavior medicine. Utilizing mobile technologies -- which include smartphones, wearable sensors, and the so-called internet of things -- his group applies these technologies to real-world social problems with global implications. These issues involve a variety of areas, such as environmental monitoring and air pollution, agriculture, farming, and global health.
Without loads of data, we have problems that not even the most intelligent machine learning systems can solve. Simple directions become extremely difficult without a destination. Navigating and processing a healthcare claim is impossible without a payer identified. Finding the best vet for a pet is difficult without knowing the species.