In this week’s roundup, understand why decision intelligence is being called the future of analytics. Learn how machine learning helps Zillow react to market trends quicker. Also, AI systems are outperforming humans in microchip floor plan designing. And nutritionists are making sure they aren’t left out of the AI revolution.
By Krishna Kallukari, contributing writer for betanews.com
Decision intelligence is a new, powerful practice of using information to make more efficient decisions at scale. Often touted as “the new business intelligence (BI),” decision intelligence promises to take the insights from dashboards a step further than just pretty charts based on data. Decision intelligence effectively extracts value from data, giving decision-makers easy-to-consume answers — often based on disparate datasets or multiple machine learning models.
By Taylor Soper, contributing writer for GeekWire.com
Seattle real estate giant Zillow Group announced new tweaks to its Zestimate tool that provides home value data on more than 104 million properties. The company now uses machine learning-based neural networks and additional data that improve how quickly the algorithm reacts to market trends.
By Andrew B. Kahng, contributing writer for Nature.com
Success or failure in designing microchips depends heavily on steps known as floor planning and placement. These steps determine where memory and logic elements are located on a chip. The locations, in turn, strongly affect whether the completed chip design can satisfy operational requirements such as processing speed and power efficiency. So far, the floor planning task, in particular, has defied all attempts at automation. It is therefore performed iteratively and painstakingly, over weeks or months, by expert human engineers. But in a paper in Nature, researchers from Google (Mirhoseini et al.1) report a machine-learning approach that achieves superior chip floor planning in hours.
By Dave Pearson, contributing writer for aiinhealthcare.com
Clinical nutritionists won’t be left out of the medical AI revolution, as researchers are exploring use cases for augmented diet optimization, food image recognition, risk prediction and diet pattern analysis.
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