These are two of the greatest challenges people face when deploying deep learning solutions. Fact is, while highly accurate, deep learning algorithms are complex and require more computation than other approaches. The analysis of massive data sets can lead to high power and heat dissipation in data centers which limits processing speeds; always-on applications can quickly drain power and memory resources in portable devices, such as smartphones and wearables. That limits real-world applications, particularly on mobile and handheld devices. One of the greatest limitations of progress in deep learning is the amount of computation available.

Robots are going to play an integral role in hospital care soon. At least that's what Julie Shah -- an MIT professor who leads the Interactive Robotics Group at the university's Computer Science and Artificial Intelligence Laboratory -- is working on.

Robots are going to play an integral role in hospital care soon. At least that's what Julie Shah -- an MIT professor who leads the Interactive Robotics Group at the university's Computer Science and Artificial Intelligence Laboratory -- is working on. Shah recently programmed a Nao robot to watch how medical assistants perform tasks so it could help assign patients to the appropriate doctor and move patients to operating rooms and beds. That may seem like a simple task, but can actually be quite complex to handle. At Mount Sinai Beth Israel, the head nurse of the labor ward coordinates 10 nurses, 20 patients, and 20 rooms at the same time.