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AI Accelerating Discovery

Artificial Intelligence still can’t compete with common sense, but its usefulness is growing quickly.

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In early April 2018, the Materials Research Society held their spring meeting and exhibit at the Phoenix, Arizona convention center. With more than 110 symposium presentations, it was difficult to select which sessions to attend. But one forum caught my eye, “AI for Materials Development”. These days AI seems to be everywhere.

As we all speculate about the impact of AI on autonomous driving and the next killer app, Carla Gomes, Professor of Computer Science and director of the Institute for Computational Sustainability at Cornell University, is focusing on large-scale constraint-based reasoning. She pointed out that AI still can’t compete with good ol’ human common sense. Human reasoning and inference planning are still lacking in most AI systems. One of the key fundamentals of AI is building a neural network that resembles the human brain. Even with the advancements of 7nm silicon technology, this is a daunting task, not to mention the complexities of software algorithms to mimic the human thought and decision process.

But in the world of materials development, AI excels. By integrating material experimentation and AI, the discovery of new materials and the application of materials in the real world is progressing at an accelerated pace. AI is capable of developing the hypotheses and—along with robotics—is following through with new scientific discovery.

Subbarao Kambhampati is Professor of Computer Science & Engineering at Arizona State University and President of AAAI (Association for the Advancement of Artificial Intelligence). He pointed out that AI has developed in reverse of a child’s mind. A child begins with visual stimulation, moves to vocal patterning and eventually reasoning. Machine learning started with the programming from human reasoning and moved to speech recognition and now image recognition.

Both speakers place a high value on the impact of AI on research science, but Kambhampati pointed out the importance of Polany’s Paradox. Michael Polany claimed that all knowledge relies on personal judgement. AI is based on algorithms and rules, but human knowledge and capability relies on skills and experiences that often lie beneath our conscious mind and are transmitted to us via culture and tradition. The main takeaway is that ‘we know more than we can tell’. Semico believes that AI is invaluable in research, but many people believe it will take a long time before AI can replace the human quality of common sense.

Semico Research recently released a Tech Brief, Artificial Intelligence: Powering the Next Generation of Processors. For more details about Semico’s report, contact Rick Vogelei.



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