Machine learning and deep learning are both forms of artificial intelligence. You can also say, correctly, that deep learning is a specific kind of machine learning. Both machine learning and deep learning start with training and test data and a model and go through an optimization process to find the weights that make the model best fit the data. Both can handle numeric (regression) and non-numeric (classification) problems, although there are several application areas, such as object recognition and language translation, where deep learning models tend to produce better fits than machine learning models.

Researchers at Alphabet’s DeepMInd describe in a new paper a machine learning hybrid approach to difficult robotics problems.

A race in autonomy poses a particular danger because the consequences of investing machines with increased intelligence and decision-making authority are largely unknown and could prove catastrophic. In their haste to match the presumed progress of likely adversaries, states might field robotic weapons with considerable autonomy well before their abilities and limitations have been fully determined, resulting in unintended fatalities or uncontrolled escalation.”

I salute the Arms Control Association … for its keen vision of the goals ahead and for its many efforts to identify and to promote practical measures that are so vitally needed to achieve them.

– Amb. Nobuyasu Abe