If you're in software development, Dr. Tom Zimmermann, a senior researcher at Microsoft Research in Redmond, wants you to be more productive, and he's here to help. Well, while productivity can be hard to measure, his research in the Empirical Software Engineering group is attempting to do just that by using insights from actual data, rather than just gut feelings, to improve the software development process. On today's podcast, Dr. Zimmermann talks about why we need to rethink productivity in software engineering, explains why work environments matter, tells us how AI and machine learning are impacting traditional software workflows, and reveals the difference between a typical day and a good day in the life of a software developer, and what it would take to make a good day typical! Tom Zimmermann: If you think of a typical software engineer at Microsoft, they spend about half of a day on development related activities, and the other half of the day is spent on other activities like coordinating with other people in meetings, sending emails… So, there's actually not that much time that they can spend on writing code, and the time they spend writing code, on a good day, it's actually only 96 minutes, and on a bad day it's, on average, 66 minutes. And half an hour writing code actually can make the difference between a bad and a good workday. Host: You're listening to the Microsoft Research Podcast, a show that brings you closer to the cutting-edge of technology research and the scientists behind it. Host: If you're in software development, Dr. Tom Zimmermann, a senior researcher at Microsoft Research in Redmond, wants you to be more productive, and he's here to help. Well, while productivity can be hard to measure, his research in the Empirical Software Engineering group is attempting to do just that by using insights from actual data, rather than just gut feelings, to improve the software development process. On today's podcast, Dr. Zimmermann talks about why we need to rethink productivity in software engineering, explains why work environments matter, tells us how AI and machine learning are impacting traditional software workflows, and reveals the difference between a typical day and a good day in the life of a software developer, and what it would take to make a good day typical! Host: You have a cool nickname. Why do people call you that? Tom Zimmermann: So, it goes back to when I started at Microsoft.

If you're in software development, Dr. Tom Zimmermann, a senior researcher at Microsoft Research in Redmond, wants you to be more productive, and he's here to help. Well, while productivity can be hard to measure, his research in the Empirical Software Engineering group is attempting to do just that by using insights from actual data, rather than just gut feelings, to improve the software development process. On today's podcast, Dr. Zimmermann talks about why we need to rethink productivity in software engineering, explains why work environments matter, tells us how AI and machine learning are impacting traditional software workflows, and reveals the difference between a typical day and a good day in the life of a software developer, and what it would take to make a good day typical! Tom Zimmermann: If you think of a typical software engineer at Microsoft, they spend about half of a day on development related activities, and the other half of the day is spent on other activities like coordinating with other people in meetings, sending emails… So, there's actually not that much time that they can spend on writing code, and the time they spend writing code, on a good day, it's actually only 96 minutes, and on a bad day it's, on average, 66 minutes. And half an hour writing code actually can make the difference between a bad and a good workday. Host: You're listening to the Microsoft Research Podcast, a show that brings you closer to the cutting-edge of technology research and the scientists behind it. Host: If you're in software development, Dr. Tom Zimmermann, a senior researcher at Microsoft Research in Redmond, wants you to be more productive, and he's here to help. Well, while productivity can be hard to measure, his research in the Empirical Software Engineering group is attempting to do just that by using insights from actual data, rather than just gut feelings, to improve the software development process. On today's podcast, Dr. Zimmermann talks about why we need to rethink productivity in software engineering, explains why work environments matter, tells us how AI and machine learning are impacting traditional software workflows, and reveals the difference between a typical day and a good day in the life of a software developer, and what it would take to make a good day typical! Host: You have a cool nickname. Why do people call you that? Tom Zimmermann: So, it goes back to when I started at Microsoft.

How AI and ML technologies fight against the growing fraud threat? What Do You Think About Artificial Intelligence? The Pentagon's AI Center Wants to Know. Apple Acquires Artificial Intelligence Company Xnor.ai--Here's

We talked to Gatefy's team of cybersecurity experts to create a prediction of events and threats that are most likely to impact 2020. You can check the result below. At first, we anticipate that some methods and threats already known and widely used by digital intruders are still on the rise. In addition, our team points out that the increasing migration to cloud platforms will probably increase the number of data breaches. Machine learning and big data are indispensable components when it comes to protection and security.

This market is absolutely teeming with chip startups, many of whom are reaching a level of maturity where they are revealing their architectures and starting to produce measurable results. As established semiconductor companies start to appreciate the importance of the AI accelerators, and the range of vertical markets AI will encroach on, will some of them look to jump-start their strategies with acquisitions? With dozens of startups at the stage where first products are being marketed and results are being unveiled, the opposite effect also applies. I spoke with Geoff Tate, CEO of Flex Logix, recently and he quoted Warren Buffett: "When the tide goes out, you can see who's been swimming naked." Not all the startups we see in the market today will be successful.

John Kibarian, president and CEO of PDF Solutions, sat down with Semiconductor Engineering to talk about the impact of data analytics on everything from yield and reliability to the inner structure of organizations, how the cloud and edge will work together, and where the big threats are in the future. SE: When did you recognize that data would be so critical to hardware design and manufacturing? Kibarian: It goes back to 2014, when we realized that consolidation in foundries was part of a bigger shift toward fabless companies. Every fabless company was going to become a systems company, and many systems companies were rapidly becoming fabless. We had been using our analytics to help customers with advanced nodes, and one of them told me that they were never going to build another factory again. Our analytics had been used for materials review board and better control of our supply chain and packaging before that.

Daniel Kahneman is winner of the Nobel Prize in economics for his integration of economic science with the psychology of human behavior, judgment and decision-making. He is the author of the popular book "Thinking, Fast and Slow" that summarizes in an accessible way his research of several decades, often in collaboration with Amos Tversky, on cognitive biases, prospect theory, and happiness. The central thesis of this work is a dichotomy between two modes of thought: "System 1" is fast, instinctive and emotional; "System 2" is slower, more deliberative, and more logical. The book delineates cognitive biases associated with each type of thinking. Subscribe to this YouTube channel or connect on: - Twitter: https://twitter.com/lexfridman

Find here a listing of the latest industry news in genomics, genetics, precision medicine, and beyond. Updates are provided on a monthly basis. Sign-Up for our newsletter and never miss out on the latest news and updates. As 2019 came to an end, Veritas Genetics struggled to get funding due to concerns it had previously taken money from China. It was forced to cease US operations and is in talks with potential buyers. The GenomeAsia 100K Project announced its pilot phase with hopes to tackle the underrepresentation of non-Europeans in human genetic studies and enable genetic discoveries across Asia. Veritas Genetics, the start-up that can sequence a human genome for less than $600, ceases US operations and is in talks with potential buyers Veritas Genetics ceases US operations but will continue Veritas Europe and Latin America. It had trouble raising funding due to previous China investments and is looking to be acquired. Illumina loses DNA sequencing patents The European Patent ...

The Pentagon's nascent center devoted to artificial intelligence research and development wants to learn more about people's perceptions of the budding technology. According to a proposed information collection notice published in the Federal Register Thursday, the Defense Department's Joint Artificial Intelligence Center is funding a RAND Corporation-led study "exploring civil-military views regarding AI and related technologies." "This data collection will help ensure [Defense's] ability to engage with leading private sector technology corporations and their employees," officials said in the notice. As is standard with federal information collections, the Pentagon must engage public feedback on whether it is necessary before the study is conducted. Defense will accept comments until March 16 on that specific matter.

Advanced analytics, artificial intelligence and machine learning are arguably the most powerful general-purpose technologies invented since the dawn of modern computing. Extracting value from these is an imperative for business and society. It requires a deeper understanding and self-reflection among leaders of human strengths and frailties in contrast to that of modern, software-based machines and algorithms, writes Ravi Bapna in this opinion piece. Bapna is a professor of business analytics and information systems at the University of Minnesota's Carlson School of Management. Companies and societies are at the precipice of rebuilding their foundations to compete in an age of advanced analytics, artificial intelligence (AI) and machine learning (ML).