Dave Hemker, CTO at Lam Research, sat down with Semiconductor Engineering to look at some of the key issues on the process and manufacturing side, and some of the key developments that will reshape the semiconductor industry in the future. What follows are excerpts of that conversation. SE: One of the big discussion topics these days is 3D NAND. How far can we go with 3D NAND? How many layers can we have?

Twilio, which functions as a communication tool for developers and businesses, will make both offerings available as add-ons through its recently announced marketplace, according to IBM. Developers who currently access Watson technology via Bluemix, IBM's cloud platform, can also use the two new Watson offerings. The new add-ons are designed to offer additional message enhancement capabilities through natural language processing to help businesses understand sentiment, keywords, entities and high-level concepts from text messages, with the goal of providing enterprises with actionable insights from the unstructured data, IBM said. The add-ons will be pre-integrated with Twilio's APIs (application program interfaces). Twilio add-ons are designed to make it easier for developers to integrate with other platforms and technologies, according to IBM.

This question has been asked on CV some yrs ago, it seems worth a repost in light of 1) order of magnitude better computing technology (e.g. Let's assume the goal is not hypothesis testing, not effect estimation, but prediction on un-seen test set. So, no weight is given to any interpretable benefit. Second, let's say you cannot rule out the relevance of any predictor on subject matter consideration, ie. Third, you're confront with (hundreds of) millions of predictors.

Wouldn't it be neat to have a brain that could read and understand all that information? There's this startup, Iris AI, that works to fix this problem by building an artificial one. This Singularity University backed team that wants to build an AI capable of highlighting new trends and interconnections of discoveries. This could help researchers in startups, corporations and research institutes to implement what is already out there. I first came across them via TED where they had processed my 2011 (gosh a lot has happened since) TED talk into their awesome system.

Are you skeptical about machines' ability to effectively aid social science decision making? Machines are becoming ever more intelligent, increasingly able to help humans make decisions across the social science spectrum, but cognitive computing is still in its infancy, with much unexplored ground ahead. Accordingly, government leaders who harness the power of cognitive computing are helping usher in a renaissance of simplified operations and enhanced constituent engagement. The secret to effectively using cognitive computing to aid human decision making lies in teaching computers to ask the right questions while taking account context and staying focused on what computers do well. Computers operate at great speed. But can we teach computers to be good social scientists?

One of Britain's leading computer scientists has criticised the "staggering sexism" in the industry, citing a visit to an artificial intelligence laboratory where a prototype of an "enhanced human" was entirely male. Ursula Martin, a professor of computer science at Oxford University, said that despite attempts to redress the problem, there was still an anti-female bias. She said that universities had attempted to encourage more women to enrol on science and mathematics courses, admitting that the institutions "did not always get it right" but they did try to remove obstacles.

Less than 100 years from now, robots will be friendly, useful participants in our homes and workplaces, predicts UBC mechanical engineering professor and robotics expert Elizabeth Croft. We will be living in a world of Wall-Es and Rosies, walking-and-talking avatars, smart driverless cars and automated medical assistants. But much work remains before robots will truly be integrated into our daily lives. In this short Q&A, Croft lays out the rules for engagement between humans and robots and explains why it's crucial to get this aspect right. What role will robots play in our lives in the future?

We're excited to introduce the latest report and prototype from our machine intelligence R&D group! In this iteration, we explore summarization, or neural network techniques for making unstructured text data computable. Making language computable has been a goal of computer science research for decades. Historically, it has been a challenge to merely collect and store data. But it's now so cheap to store data that we often have the opposite problem: once we've data, how should we analyze it to find meaning and insights?

In this article we introduce modern statistical machine learning and bioinformatics approaches that have been used in learning statistical relationships from big data in medicine and behavioral science that typically include clinical, genomic (and proteomic) and environmental variables. Every year, data collected from biomedical and behavioral science is getting larger and more complicated. Thus, in medicine, we also need to be aware of this trend and understand the statistical tools that are available to analyze these datasets. Many statistical analyses that are aimed to analyze such big datasets have been introduced recently. However, given many different types of clinical, genomic, and environmental data, it is rather uncommon to see statistical methods that combine knowledge resulting from those different data types. To this extent, we will introduce big data in terms of clinical data, single nucleotide polymorphism and gene expression studies and their interactions with environment. In this article, we will introduce the concept of well-known regression analyses such as linear and logistic regressions that has been widely used in clinical data analyses and modern statistical models such as Bayesian networks that has been introduced to analyze more complicated data. Also we will discuss how to represent the interaction among clinical, genomic, and environmental data in using modern statistical models. We conclude this article with a promising modern statistical method called Bayesian networks that is suitable in analyzing big data sets that consists with different type of large data from clinical, genomic, and environmental data.

Welcome to Visual Evidence, a new regular series about visualization in the real world! We'll take a look at unexpected datasets, cool design solutions or insightful graphics. We'll find examples of how visual information can help us solve real-world problems or save us from our own mistakes. And we'll illustrate all these ideas with charts, sketches, and of course, plenty of gifs. Chances are, you probably think your mind works pretty well. It might lead you astray now and then, but usually it helps you make good decisions and remember things reliably. At the very least, you're probably confident that it doesn't change depending on the time of day or what you had to eat.