To celebrate International Women's Day, we take a look back over the past year of AIhub content and highlight some of our favourite articles, interviews, podcasts and videos, by, or featuring, women in the field. Falaah Arif Khan is an engineer/scientist by training and an artist by nature. She is currently Artist-in-Residence at the Center for Responsible AI at New York University. When we interviewed Falaah in 2020 she had just completed her first comic book, Meet AI. She has since teamed up with other AI researchers on other exciting projects.

In 2017, I returned to Canada from Sweden, where I had spent a year working on automation in mining. Shortly after my return, the New York Times published a piece headlined The Robots Are Coming, and Sweden Is Fine, about Sweden's embrace of automation while limiting human costs. Although Swedes are apparently optimistic about their future alongside robots, other countries aren't as hopeful. One widely cited study estimates 47 per cent of jobs in the United States are at risk of being replaced by robots and artificial intelligence. Whether we like it or not, the robot era is upon us.

New work by computer scientists at Lawrence Livermore National Laboratory (LLNL) and IBM Research on deep learning models to accurately diagnose diseases from X-ray images with less labeled data won the Best Paper award for Computer-Aided Diagnosis at the SPIE Medical Imaging Conference on Feb. 19. The technique, which includes novel regularization and "self-training" strategies, addresses some well-known challenges in the adoption of artificial intelligence (AI) for disease diagnosis, namely the difficulty in obtaining abundant labeled data due to cost, effort or privacy issues and the inherent sampling biases in the collected data, researchers said. AI algorithms also are not currently able to effectively diagnose conditions that are not sufficiently represented in the training data. LLNL computer scientist Jay Thiagarajan said the team's approach demonstrates that accurate models can be created with limited labeled data and perform as well or even better than neural networks trained on much larger labeled datasets. The paper, published by SPIE, included co-authors at IBM Research Almaden in San Jose.

Even with technology, sometimes we believe in fairy tales. A fairy tale is a story with a "fantastic and magical setting or magical influences within a story." I hadn't thought much about fairy tales recently, until I began reviewing the number of online case studies about artificial intelligence (AI) in companies. In most of these case studies, the bottom line was that an AI solution had been successfully implemented. However, when I reviewed the stories for business outcomes or results, the results weren't there.

And I also think there's going to be a different conversation about AI, machine learning and things like that.

I recently started an AI-focused educational newsletter, that already has over 70,000 subscribers. TheSequence is a no-BS (meaning no hype, no news etc) ML-oriented newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. One of the pivotal challenges of the next decade of artificial intelligence(AI) is to determine whether data and intelligence are democratized or remain in control of a few large organizations. A few months ago, I wrote a three-part series of the decentralization of artificial intelligence(AI).

This book lays a new foundation toward achieving artificial self-intelligence by future machines such as intelligent vehicles. Its chapters provide broad coverage of the three key modules behind the design and development of intelligent vehicles for the ultimate purpose of actively ensuring safe driving as well as accident prevention. Self-contained and unified in presentation, the book explains in detail the fundamental solutions to autonomous vehicles' perception, decision-making, and action-taking, in a pedagogic order.

I worry that when writing these columns, I sometimes start by meandering my way off into the weeds, cogitating and ruminating on "this and that" before eventually bringing the story back home. So, on the basis that "a change is as good as a rest," as the old English proverb goes, let's do things a little differently this time. Take a look at the image below. What do you see in addition to the penny piece? What I see is a Mantis AI-in-Sensor (AIS) System-on-Chip (SoC), where the "AI" portion of this moniker stands for "artificial intelligence."

Hey guys, in this module, we are going to talk about What are the Applications of Python? We have already seen the Future of Python in our previous module and know already how python is ruling today's industry and how it is becoming famous. So, let's focus on some of the applications in this module. Let's dive into the depth of this module. Since we know what python is and how one can start his/her career in the same, so, let's see applications of the python that in what manner python is useful or what all things can be constructed or built from python.

The last few years saw customers shift their preference from buying clothes at retail outlets to conveniently buying online on their gadgets. This change in customer behavior was further accelerated by COVID-19 as customers had to depend on buying online for most of their shopping needs. Despite COVID-19 beginning to ease off slowly, customers believe that shopping online is a lot more convenient than having to go from store to store to get what they're looking for. E-commerce brands are thus doing their best to keep their hard-earned customers engaged on their platform by delivering personalized user experiences. We caught up with Mohit Panjwani, Associate Director- Revenue Growth at Myntra to understand how e-commerce fashion brands like Myntra are leveraging the power of customer data and marketing analytics to craft memorable and personalized shopping experiences.