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Interview with Kaggle Expert Mani Sarkar, PyDataGlobal starts Thursday

#artificialintelligence

Did you know that Holiday Extras are hiring a Senior Data Engineer? See their ad and many more below. PyDataGlobal runs from tomorrow, the schedule is online. I'm running another Executives at PyData session on Thursday afternoon - please join and bring your boss! Below I've got the first of a two part interview with Kaggle Expert Mani Sarkar, I want to bring in some deeper expertise to this newsletter so I'm going to run some of these interviews.


Interview with Daniela Duca on creating SAGE Texti: A free tool for cleaning and pre-processing textual data

#artificialintelligence

As part of a three-month focus on data analysis MethodSpace is featuring original writings, podcasts, and videos on all phases of the process for both qualitative and quantitative studies. In this interview, learn about a free tool, SAGE Texti. Q: Hi Daniela, can you tell us a little about your background? I have a multi-disciplinary background, starting off in biochemistry, then moving into economics. I wrote my PhD in corporate innovation, while working in fintech to support the development of data crawlers and algorithms.


Domino Data Lab Launches Inaugural Partner Program Targeting Service, Technology Providers

#artificialintelligence

Domino Data Lab is launching the company's first partner program today as the data science and MLOps software developer looks to scale up its work with service and technology partners and provide them with a structured program with more resources and benefits. The new Domino Partner Network will provide structure for what has largely been ad hoc partner processes, according to Domino executives. It will help the company expand and scale its work with partners and provide them with needed training, go-to-market resources and incentives. "Our push to formalize this partner program and to work with a wider range of partners is really being driven by our customers," CEO Nick Elprin said in an interview with CRN. "At a high level, what we're doing is formalizing an approach and a structure for how we work with partners."


A Decision Model for Decentralized Autonomous Organization Platform Selection: Three Industry Case Studies

arXiv.org Artificial Intelligence

Decentralized autonomous organizations as a new form of online governance arecollections of smart contracts deployed on a blockchain platform that intercede groupsof people. A growing number of Decentralized Autonomous Organization Platforms,such as Aragon and Colony, have been introduced in the market to facilitate thedevelopment process of such organizations. Selecting the best fitting platform ischallenging for the organizations, as a significant number of decision criteria, such aspopularity, developer availability, governance issues, and consistent documentation ofsuch platforms, should be considered. Additionally, decision-makers at theorganizations are not experts in every domain, so they must continuously acquirevolatile knowledge regarding such platforms and keep themselves updated.Accordingly, a decision model is required to analyze the decision criteria usingsystematic identification and evaluation of potential alternative solutions for adevelopment project. We have developed a theoretical framework to assist softwareengineers with a set of Multi-Criteria Decision-Making problems in software production.This study presents a decision model as a Multi-Criteria Decision-Making problem forthe decentralized autonomous organization platform selection problem. Weconducted three industry case studies in the context of three decentralizedautonomous organizations to evaluate the effectiveness and efficiency of the decisionmodel in assisting decision-makers.


The robots are coming for your office

#artificialintelligence

As the editor-in-chief of The Verge, I can theoretically assign whatever I want. However, there is one topic I have failed to get people at The Verge to write about for years: robotic process automation, or RPA. RPA isn't robots in factories, which is often what we think of when it comes to automation. This is different: RPA is software. Software that uses other software, like Excel or an Oracle database. On this week's Decoder, I finally found someone who wants to talk about it with me: New York Times tech columnist Kevin Roose. His new book, Futureproof: 9 Rules for Humans in the Age of Automation, has just come out, and it features a lengthy discussion of RPA, who's using it, who it will affect, and how to think about it as you design your career. What struck me during our conversation were the jobs that Kevin talks about as he describes the impact of automation: they're not factory workers and truck drivers. If you have the kind of job that involves sitting in front of a computer using the same software the same way every day, automation is coming for you. It won't be cool or innovative or even work all that well -- it'll just be cheaper, faster, and less likely to complain. That might sound like a downer, but Kevin's book is all about seeing that as an opportunity. You'll see what I mean. Okay, Kevin Roose, tech columnist, author, and the only reporter who has ever agreed to talk to me about RPAs. This transcript has been lightly edited for clarity. Kevin Roose, you're a tech columnist at The New York Times and you have a new book, Futureproof: 9 Rules for Humans in the Age of Automation, which is out now. Thank you for having me. You're ostensibly here to promote your book, which is great. But there's one piece of the book that I am absolutely fascinated by, which is this thing called "robotic process automation." And I'm gonna do my best with you on this show, today, to make that super interesting. But before we get there, let's talk about your book for a minute. What is your book about? Because I read it, and it has a big idea and then there's literally nine rules for regular people to survive. So, tell me how the book came together. So, the book is basically divided into two parts.


Spotlight Interview with Dr Thomas Sander from Idorsia Pharmaceuticals - Collaborative Drug Discovery Inc. (CDD)

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Dr. Sander kindly agreed to give us this interview at the Idorsia headquarters in Basel, Switzerland. Asking the questions from CDD are Neil Chapman and Mariana Vaschetto. By education I am organic chemist. During my seventh year at school we started to have chemistry classes and soon I had made up my mind to study chemistry. Four years later while still at school I had an opportunity to access the local University's Tectronix graphics computers.


Signals & Threads - Build Systems

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Welcome to Signals & Threads, in-depth conversations about every layer of the tech stack, from Jane Street. Today, I'm going to have a conversation with Andrey Mokhov about build systems. Build systems are an important but I think poorly understood and often unloved part of programming. Developers often end up with only a hazy understanding of what's going on with their build system learning just enough to figure out what arcane invocation they need to get the damn thing working and then stop thinking about it at that point, and that's a shame because build systems matter a lot to our experience as developers. A lot of what underlies a good developer experience really comes out of the build system that you use and also there's a lot of beautiful ideas and structure inside of build systems. Sadly, a lot of that beauty is obscured by a complex thicket of messy systems of different kinds and a complicated ecosystem of different build systems for different purposes, and I'm hoping that ...


GPT-3 Creative Fiction

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What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.


The productive software engineer with Dr. Tom Zimmermann Learn More

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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.


The productive software engineer with Dr. Tom Zimmermann Learn More

#artificialintelligence

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.