This is Part 2 of the Interview Question series that I recently started. In Part 1, we talked about another important data science interview question pertaining to scaling your ML model. Be sure to check that out! A typical open-ended question that often comes up during interviews (both first and second round) is related to your personal (or side) projects. And trust me when I say this, this question is the best thing that can happen to you during an interview.

CNN appears to be making it common practice not to report news that may portray presumptive Democratic nominee Joe Biden in a negative light. Biden raised eyebrows Wednesday morning during an interview with a virtual panel at the convention of the National Association of Black Journalists and National Association of Hispanic Journalists, which was posted in full on Thursday, where he was asked if he has taken a cognitive test. "No, I haven't taken a test. Why the hell would I take a test?" Biden reacted to the question from CBS reporter Errol Barnett, who is Black.

All of our Presentations are ready and you can begin watching them today via our Online Events Page here in Eventbrite. On November 3rd, we will have a Live Q&A with our speakers. This is the perfect time to ask them questions and go in depth on their topic of expertise. On November 4th & 5th, we will host a live join the live workshop which you teach you what you need to know to make a great Chatbot using Dialogflow and Botsociety. We will also make free pre-workshop available to you to help you get set up and answer all of the basic questions about the technology you'll be using.

Machine learning (ML) is a promising enabler for the fifth generation (5G) communication systems and beyond. By imbuing intelligence into the network edge, edge nodes can proactively carry out decision-making, and thereby react to local environmental changes and disturbances while experiencing zero communication latency. To achieve this goal, it is essential to cater for high ML inference accuracy at scale under time-varying channel and network dynamics, by continuously exchanging fresh data and ML model updates in a distributed way. Taming this new kind of data traffic boils down to improving the communication efficiency of distributed learning by optimizing communication payload types, transmission techniques, and scheduling, as well as ML architectures, algorithms, and data processing methods. To this end, this article aims to provide a holistic overview of relevant communication and ML principles, and thereby present communication-efficient and distributed learning frameworks with selected use cases.

We fell in love with the LIFX Z LED strip lights for their incredibly simple set up, ease of use, and variety of awesome features. The Lifx Z LED Strip Kit impressed us from the very beginning. In what felt like a blink of an eye, we had these dimmable lights connected to Alexa, Google Assistant, and Siri. It also works with IFTTT, SmartThings, Nest, Arlo, Flic, and more. The strip is very responsive when controlled using the Lifx app on iOS and Android devices (keep in mind that HomeKit is only available on Apple smartphones and tablets). Although these weren't the brightest lights we tested, they put off a vivid glow that easily illuminates a dark room. These smart lights have a noticeably thicker strip than the set from Govee, but less chunky than the C by GE set we tested.

In unsupervised domain adaptation (UDA), a classifier for the target domain is trained with massive true-label data from the source domain and unlabeled data from the target domain. However, collecting fully-true-label data in the source domain is high-cost and sometimes impossible. Compared to the true labels, a complementary label specifies a class that a pattern does not belong to, hence collecting complementary labels would be less laborious than collecting true labels. Thus, in this paper, we propose a novel setting that the source domain is composed of complementary-label data, and a theoretical bound for it is first proved. We consider two cases of this setting, one is that the source domain only contains complementary-label data (completely complementary unsupervised domain adaptation, CC-UDA), and the other is that the source domain has plenty of complementary-label data and a small amount of true-label data (partly complementary unsupervised domain adaptation, PC-UDA). To this end, a complementary label adversarial network} (CLARINET) is proposed to solve CC-UDA and PC-UDA problems. CLARINET maintains two deep networks simultaneously, where one focuses on classifying complementary-label source data and the other takes care of source-to-target distributional adaptation. Experiments show that CLARINET significantly outperforms a series of competent baselines on handwritten-digits-recognition and objects-recognition tasks.

At Landing AI, we are building next generation AI products and solutions to help transform traditional industries like manufacturing and agriculture. This is an ambitious goal that requires close collaboration between people from different disciplines, including product, machine learning engineers and software engineers. In this blog post, we talk with some of our software engineers, who play an important role in building and executing AI solutions, to get their perspectives on what a software engineer's work life is like at Landing AI. What is it like working as a SDE at Landing AI? What's your typical day look like? Pingyang: Besides designing and building various kinds of innovative systems and solutions, I have also been spending a lot of my time learning new things that I was not able to learn elsewhere. I got more opportunities to build tools and frameworks that I'm not allowed to touch or modify in big companies.

A lot of the people who are working at the many AI chip startups have a long history in processor development in the datacenter, and that is certainly true of the folks who founded SambaNova Systems. And this is a fortunate thing because these people can leverage some of the good ideas they know worked when commercializing a new technology and avoid some of the big mistakes their former employers sometimes made. At our recent The Next AI Platform event, we sat down with Rodrigo Liang, co-founder and chief executive officer of SambaNova Systems, which is one of the upstart custom AI chip producers vying for attention and budget dollars. SambaNova was founded in 2017 by a bunch of ex-Sun Microsystems techies as well as a few from Stanford University, which is of course where Sun itself was born in 1982. The co-founders include Kunle Olukotun and Chris Ré, professors at Stanford, with Olukotun being the leader of the Hydra chip multiprocessor research project and sometimes known as the father of the multicore processor.

MLOps is a relatively new concept in the AI (Artificial Intelligence) world and stands for "machine learning operations." Its about how to best manage data scientists and operations people to allow for the effective development, deployment and monitoring of models. "MLOps is the natural progression of DevOps in the context of AI," said Samir Tout, who is a Professor of Cybersecurity at the Eastern Michigan University's School of Information Security & Applied Computing (SISAC). "While it leverages DevOps' focus on security, compliance, and management of IT resources, MLOps' real emphasis is on the consistent and smooth development of models and their scalability." The origins of MLOps goes back to 2015 from a paper entitled "Hidden Technical Debt in Machine Learning Systems."

Roorkee: Indian Institute of Technology, Roorkee (IIT-R) alumnus Dr Sunil Kumar Vuppala has won the prestigious Zinnov Award 2020 for his contribution to the field of Artificial Intelligence and Big Data Analytics. Vuppala was from 2004 batch of Department of Electronics and Communication Engineering of IIT-R. The objective of the award is to recognise the contribution of individuals as well as the organisations that enable business innovation along with diversity. Vuppala, a data scientist, is currently serving as the Director of Data Science at Ericsson. "I am elated to have received the award. The tech-driven education at IIT Roorkee laid the foundation for my strong fundamentals in the emerging technologies domain," said Vuppala.