Despite big successes in artificial intelligence (AI) and deep learning, there have been critical assessments made to current deep learning methods.8 Deep learning is data hungry, has limited knowledge transfer capabilities, does not quickly adapt to changing tasks or distributions, and insufficiently incorporates world or prior knowledge.1,3,8,14 While deep learning excels in natural language processing and vision benchmarks, it often underperforms at real-world applications. Deep learning models were shown to fail at new data, new applications, deployments in the wild, and stress tests.4,5,7,13,15 Therefore, practitioners harbor doubt over these models and hesitate to employ them in real-world application.
Current AI research has tried to overcome the criticisms and limitations of deep learning. AI research and machine learning in particular aims at a new level of AI—a “broad AI”—with considerably enhanced and broader capabilities for skill acquisition and problem solving.3 We contrast “broad AI” to “narrow AI,” which are the AI systems currently applied. A broad AI considerably surpasses a narrow AI in the following essential properties: knowledge transfer and interaction, adaptability and robustness, abstraction and advanced reasoning, and efficiency (as illustrated in the accompanying figure). A broad AI is a sophisticated and adaptive system, which successfully performs any cognitive task by virtue of its sensory perception, previous experience, and learned skills.
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