Challenges and opportunities: from big data to knowledge in AI 2.0
| Autor: | Fei Wu, Yueting Zhuang, Chun Chen, Yunhe Pan |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Computer Networks and Communications Computer science business.industry AI-complete Big data Context (language use) 02 engineering and technology Symbolic artificial intelligence Nouvelle AI Data science Artificial intelligence situated approach 020901 industrial engineering & automation Hardware and Architecture Artificial general intelligence Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Applications of artificial intelligence Electrical and Electronic Engineering business |
| Zdroj: | Frontiers of Information Technology & Electronic Engineering. 18:3-14 |
| ISSN: | 2095-9230 2095-9184 |
| DOI: | 10.1631/fitee.1601883 |
| Popis: | In this paper, we review recent emerging theoretical and technological advances of artificial intelligence (AI) in the big data settings. We conclude that integrating data-driven machine learning with human knowledge (common priors or implicit intuitions) can effectively lead to explainable, robust, and general AI, as follows: from shallow computation to deep neural reasoning; from merely data-driven model to data-driven with structured logic rules models; from task-oriented (domain-specific) intelligence (adherence to explicit instructions) to artificial general intelligence in a general context (the capability to learn from experience). Motivated by such endeavors, the next generation of AI, namely AI 2.0, is positioned to reinvent computing itself, to transform big data into structured knowledge, and to enable better decision-making for our society. |
| Databáze: | OpenAIRE |
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