Artificial Intelligence (AI) is one of the most rapidly growing domains today. Keeping track and taking stock of AI requires not just constant attention, but also the ability to dissect and evaluate across a number of dimensions.
Reinforcement learning (RL) is an area of machine learning that has received lots of attention from researchers over the past decade. It’s been also a big year in natural language processing (NLP): Google AI’s BERT and Transformer; Allen Institute’s ELMo; OpenAI’s Transformer, Ruder and Howard’s ULMFiT, and Microsoft’s MT-DNN demonstrated that pre-trained language models can substantially improve performance on a variety of NLP tasks.
In the future, combining deep learning and domain knowledge is a fruitful avenue of exploration: “Especially when the goal of an AI project is to solve a real-world problem vs. building a general intelligence agent that should learn to solve a talk.” According to AI Ethics report from AI World Society (AIWS), AI can be a force for helping people achieve well-being and happiness, unleash their potential, obtain greater freedom, relieve them of resource constraints and arbitrary/inflexible rules and processes, and solve important issues, such as SDGs