The path to real-world artificial intelligence
Experts from MIT and IBM held a webinar this week to discuss where AI technologies are today and advances that will help make their usage more practical and widespread. Image: Sompong Rattanakunchon / Getty Images More about artificial intelligence Artificial intelligence has made significant strides in recent years, but modern AI techniques remain limited, a panel of MIT professors and IBM's director of the Watson AI Lab said during a webinar this week. Neural networks can perform specific, well-defined tasks but they struggle in real-world situations that go beyond pattern recognition and present obstacles like limited data, reliance on self-training, and answering questions like "why" and "how" versus "what," the panel said. The future of AI depends on enabling AI systems to do something once considered impossible: Learn by demonstrating flexibility, some semblance of reasoning, and/or by transferring knowledge from one set of tasks to another, the group said. SEE: Robotic process automation: A cheat sheet (free PDF) (TechRepublic) The panel discussion was moderated by David Schubmehl, a research director at IDC, and it began with a question he posed asking about the current limitations of AI and machine learning. Read the full article