摘要： The journey of data analytics began with simple descriptive (review of past events) and diagnostic (analysis of past events) exercises and moved to the more sophisticated predictive and prescriptive genres,where advanced data models have enabled accurate future forecasts and actionable intelligence. And now cognitive computing has further strengthened the actionable predictive power of the machines by making the machine act like the human brain. For example, in addition to deciphering “words” in a piece of text, a cognitive analytics system can also interpret the context of the written material.
摘要： In self-supervised learning, an AI technique where the training data is automatically labeled by a feature extractor, the said extractor not uncommonly exploits low-level features (known as “shortcuts”) that cause it to ignore useful representations. In search of a technique that might help to remove those shortcuts autonomously, researchers at Google Brain developed a framework — a “lens” — that makes changes enabling self-supervised models to outperform those trained in a conventional fashion.
摘要： Design science research (hereafter DSR) is a relatively new approach to research (Reubens, 2016) with a goal to construct a new reality (i.e. solve problems) instead of explaining an existing reality, or helping to make sense of it (Iivari and Venable, 2009).
摘要： Generative AI language models like OpenAI’s GPT-2 produce impressively coherent and grammatical text, but controlling the attributes of this text — such as the topic or sentiment — requires architecture modification or tailoring to specific data. That’s why a team of scientists at Uber, Caltech, and the Hong Kong University of Science and Technology devised what they call the Plug and Play Language Model (PPLM), which combines a pretrained language model with one or more attribute classifiers that guide novel text generation.
摘要： While much work in data science to date has focused on algorithmic scale and sophistication, safety — that is, safeguards against harm — is a domain no less worth pursuing. This is particularly true in applications like self-driving vehicles, where a machine learning system’s poor judgment might contribute to an accident.
