Self-driving cars are one of the most hotly debated topics when it comes to vehicle safety. Plenty of companies are using autonomous vehicles for various purposes. However, most Americans don't think they're all that safe. Additionally, with videos showing the faults of some of these systems, it's easy to see the hesitation. So, it will undoubtedly get better over time.

Machine learning is an intimidating subject. Knowing where to develop mastery around such a massive subject that encompasses so many fields, research topics, and applications can be the hardest part of the journey. Anyone with a background in programming will attest to the value of a good textbook, especially when it comes to a subject as technical as machine learning. Get a quote for an end-to-end data solution to your specific requirements. Whether you're a complete novice or a distinguished mastermind in this field, we at iMerit have compiled the best field guides, icebreakers, and referential machine learning textbooks that will suit both newcomers and veterans alike who are looking to improve their understanding of machine learning.

Money can't buy you happiness (although you can reportedly lease it for a while). It definitely cannot buy you love. And the rumor is money also cannot buy you large troves of labeled data that are ready to be plugged into your particular AI use case, much to the chagrin of former Apple product manager Ivan Lee. "I spent hundreds of millions of dollars at Apple gathering labeled data," Lee said. "And even with its resources, we were still using spreadsheets."

The Chicago Cubs won the US Major League Baseball World Series title in 2016, its first win in 108 years. The LA Dodgers reached the 2017 World Series final, before losing in a game tainted by a cheating scandal. What the two teams shared in their dream runs was use of AI. Florida-based Kinatrax had high-speed cameras installed at strategic points on baseball grounds for synchronized motion-capture videos of pitchers. These were annotated, tagged and analysed to create the 3D anatomical models that fine-tuned pitching mechanics for each player.

Data annotation and enrichment company, iMerit has raised $20 million dollars in Series B funding. The raise comes amid growing demand in the $1.6 billion dollar A.I data annotation market. For a first on Cheddar, we spoke to Radha Basu, the CEO and founder of iMerit.

Abstract: Wherever you are in your team's machine learning journey, it's helpful to think about evolving towards large scale production. A key ingredient of this journey is your data labeling and annotation framework. In this talk we focus on how to build your data labeling pipeline to be enterprise grade. We will describe the considerations and insights that go into making your data pipeline a mindful part of your development pipeline. Proactively planning a data process can generate progressively better results during development, but it requires some thought and stakeholder buy-in.

Five years ago, Hyderabad resident Tulasi Mathi was forced to quit her job as a maths teacher due to health issues and the birth of her two children. But today, the 29-year-old does data labelling and makes up to Rs 15,000 a month. The money isn't much but it's more than she made as a teacher, and enough to pay her kids' school fees and her own expenses. Today, she scans videos and marks and labels objects encountered by self-driving cars. Her output is used to train artificial intelligence algorithms powering such cars. All Mathi knows is that it makes her life easier.

Five years ago, Hyderabad resident Tulasi Mathi was forced to quit her job as a maths teacher due to health issues and the birth of her two children. But today, the 29-year-old does data labelling and makes up to Rs 15,000 a month. The money isn't much but it's more than she made as a teacher, and enough to pay her kids' school fees and her own expenses.She chanced on data labelling work through a YouTube video in 2017. Today, she scans videos and marks and labels objects encountered by self-driving cars. Her output is used to train artificial intelligence algorithms powering such cars. All Mathi knows is that it makes her life easier.

There is a growing demand for data-labelling services that are "localised"- both linguistically and culturally relevant to India From an opportunity point of view, there are about a lakh jobs posted on various portals currently There is a growing demand for data-labelling services that are "localised"- both linguistically and culturally relevant to India NEW DELHI: Five years ago, Hyderabad resident Tulasi Mathi was forced to quit her job as a maths teacher due to health issues and the birth of her two children. But today, the 29-year-old does data labelling and makes up to Rs 15,000 a month. The money isn't much but it's more than she made as a teacher, and enough to pay her kids' school fees and her own expenses. Today, she scans videos and marks and labels objects encountered by self-driving cars. Her output is used to train artificial intelligence algorithms powering such cars. All Mathi knows is that it makes her life easier.

Namita Pradhan sat at a desk in downtown Bhubaneswar, India, about 40 miles from the Bay of Bengal, staring at a video recorded in a hospital on the other side of the world. The video showed the inside of someone's colon. Pradhan was looking for polyps, small growths in the large intestine that could lead to cancer. When she found one -- they look a bit like a slimy, angry pimple -- she marked it with her computer mouse and keyboard, drawing a digital circle around the tiny bulge. She was not trained as a doctor, but she was helping to teach an artificial intelligence system that could eventually do the work of a doctor. Pradhan was one of dozens of young Indian women and men lined up at desks on the fourth floor of a small office building. They were trained to annotate all kinds of digital images, pinpointing everything from stop signs and pedestrians in street scenes to factories and oil tankers in satellite photos. AI, most people in the tech industry would tell you, is the future of their industry, and it is improving fast thanks to something called machine learning. But tech executives rarely discuss the labor-intensive process that goes into its creation.