【英文】剑桥大学报告：2019年度AI全景报告State of AI Report（135页）

Artifificial intelligence (AI): A broad discipline with the goal of creating intelligent machines, as opposed to the  natural intelligence that is demonstrated by humans and animals. It has become a somewhat catch all term that  nonetheless captures the long term ambition of the fifield to build machines that emulate and then exceed the full  range of human cognition. Machine learning (ML): A subset of AI that often uses statistical techniques to give machines the ability to "learn"  from data without being explicitly given the instructions for how to do so. This process is known as “training” a  “model” using a learning “algorithm” that progressively improves model performance on a specifific task. Reinforcement learning (RL): An area of ML that has received lots of attention from researchers over the past  decade. It is concerned with software agents that learn goal-oriented behavior by trial and error in an environment  that provides rewards or penalties in response to the agent’s actions (called a “policy”) towards achieving that goal. Deep learning (DL): An area of ML that attempts to mimic the activity in layers of neurons in the brain to learn how  to recognise complex patterns in data. The “deep” in deep learning refers to the large number of layers of neurons in  contemporary ML models that help to learn rich representations of data to achieve better performance gains.

Algorithm: An unambiguous specifification of how to solve a particular problem. Model: Once a ML algorithm has been trained on data, the output of the process is known as the model. This can  then be used to make predictions. Supervised learning: This is the most common kind of (commercial) ML algorithm today where the system is  presented with labelled examples to explicitly learn from. Unsupervised learning: In contrast to supervised learning, the ML algorithm has to infer the inherent structure of  the data that is not annotated with labels. Transfer learning: This is an area of research in ML that focuses on storing knowledge gained in one problem and  applying it to a different or related problem, thereby reducing the need for additional training data and compute.  Natural language processing (NLP): Enables machines to analyse, understand and manipulate textual data.  Computer vision: Enabling machines to analyse, understand and manipulate images and video.