A few posts ago we talked about A/B testing with multiple metrics. We used the famous Bonferroni Correction to control our family-wise error rate when performing multiple tests. The Bonferroni method is widely used for its simplicity and broad applicability. We know its biggest drawback is that it is a conservative test. A finding that survives a Bonferroni adjustment is a credible trial outcome.

Deep learning is often recognized as the magic behind self-driving cars and facial recognition, but what about its ability to safeguard the quality of the materials that make up these advanced devices? Carnegie Mellon University Professor of Materials Science and Engineering Elizabeth Holm and materials science and engineering doctoral student Bo Lei have adopted computer vision methods for microstructural images that not only require a fraction of the data deep learning typically relies on, but can save materials researchers an abundance of time and money. Quality control in materials processing requires the analysis and classification of complex material microstructures. For instance, the properties of some high-strength steels depend on the amount of lath-type bainite in the material. However, the process of identifying bainite in microstructural images is time-consuming and expensive as researchers must first use two types of microscopy to take a closer look and then rely on their own expertise to identify bainitic regions.

Multiple testing adjustment has gained in popularity with large scale datasets used for exploratory purposes. It is now a key consideration in statistical inference problems.

Dozens of employers looking to hire the next generation of tech employees descended on the University of California, Berkeley in September to meet students at an electrical engineering and computer science career fair. Boris Yue, 20, was one of thousands of student attendees, threading his way among fellow job-seekers to meet recruiters. But Yue wasn't worried about so much potential competition. While the job outlook for those with computer skills is generally good, Yue is in an even more rarified category: he is studying artificial intelligence, working on technology that teaches machines to learn and think in ways that mimic human cognition. His choice of specialty makes it unlikely he will have difficulty finding work.

SAN FRANCISCO (Reuters) - Dozens of employers looking to hire the next generation of tech employees descended on the University of California, Berkeley in September to meet students at an electrical engineering and computer science career fair. Boris Yue, 20, was one of thousands of student attendees, threading his way among fellow job-seekers to meet recruiters. But Yue wasn't worried about so much potential competition. While the job outlook for those with computer skills is generally good, Yue is in an even more rarified category: he is studying artificial intelligence, working on technology that teaches machines to learn and think in ways that mimic human cognition. His choice of specialty makes it unlikely he will have difficulty finding work.

Liz Holm, a materials science and engineering professor at Carnegie Melon, saw the increased demand first-hand in May, when one of her graduating PhD students, who used machine learning methods for her research, was overwhelmed with job offers, none of which were in materials science and all of them AI-related. Eventually the student took a job with Proctor & Gamble, where she uses AI to figure out where to put items on store shelves around the globe. "Companies are really hungry for these folks right now," Holm said.

Elizabeth Holm, a professor of materials science and engineering at the College of Engineering at Carnegie Mellon University and a computational materials scientist at Sandia National Laboratories says we're in the midst of an artificial intelligence (AI) culture shift. She also says that machines won't replace human experts. "Machines are great at handling things, like large amounts of data, but machines still need an expert, a human, to analyze the data, set parameters and guide decisions," said Holm. "Engineering and science decisions are based on understanding how things work. How does a bridge support its load? How does an engine convert fuel into motion? In contrast, AI transforms data into decisions without understanding any underlying principles," said Holm. "Applying AI to engineering and science will require a culture shift: either we will learn to trust decisions that we do not understand, or AIs will evolve to base their decisions on principles that humans can interpret and control."

Elizabeth Holm, a professor of materials science and engineering at the College of Engineering at Carnegie Mellon University and a computational materials scientist at Sandia National Laboratories says we're in the midst of an artificial intelligence (AI) culture shift. She also says that machines won't replace human experts. "Machines are great at handling things, like large amounts of data, but machines still need an expert, a human, to analyze the data, set parameters and guide decisions," said Holm. "Engineering and science decisions are based on understanding how things work. How does a bridge support its load? How does an engine convert fuel into motion? In contrast, AI transforms data into decisions without understanding any underlying principles," said Holm. "Applying AI to engineering and science will require a culture shift: either we will learn to trust decisions that we do not understand, or AIs will evolve to base their decisions on principals that humans can interpret and control."

Artificial intelligence (AI) made incredible strides in 2016, and the growth appears set to accelerate as we enter the New Year. A team of Microsoft researchers has released a dataset of 100,000 questions and answers that other AI researchers can use – for free – in their quest to create systems that can read and answer questions as well as a human. The MS MARCO dataset is based on anonymized real-world data from Bing and Cortana queries and is part of an attempt to spur the breakthroughs in machine reading that are already happening in image and speech recognition. The move is also aimed at facilitating advances toward "artificial general intelligence," or machines that can think like humans – and can read and understand a document as well as a person. Meanwhile, AI helped a musician in Norway sing a new tune for the holidays this year: a Christmas carol that was created by Microsoft's AI technology.

Whether you realize it or not, machine learning is making your online experience more efficient. The technology, designed by computer scientists, is used to better understand, analyze, and categorize data. When you tag your friend on Facebook, clear your spam filter, or click on a suggested YouTube video, you're benefitting from machine learning algorithms. Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science.