Machine learning (ML) isn’t new. However, the field of big data is revitalizing the subject and more organizations are relying on ML models to scale their operations, support staff in working better and faster, to uncover hidden insights from data, or even confirm and challenge underlying assumptions. This is creating widespread interest in related topics with the C-suite, and across business lines and job roles, as enterprises embrace the value of artificial intelligence (AI) and ML.
To make a disruptive organizational impact, AI and ML need to be understood and trusted. By consulting these ML blogs from authoritative individuals and organizations that satisfy different skill levels, readers can increase their understanding and comfort level in these areas, get pressing questions answered, and connect with others who have experience using them to great benefit.
Contents
1. OpenAI
OpenAI comes from industry experts who want to bring AI to the masses. It’s linked to the non-profit research company OpenAI, co-chaired by Elon Musk and Sam Altman, and sponsored by companies such as Amazon Web Services, Microsoft, and Infosys who are trying to make AI accessible—hence the name. Contributors discuss their collective efforts to promote and advance AI technologies through long-term research. It’s a valuable resource for anyone interested in the future of AI.
2. Distill
Distill concentrates on making ML and AI more accessible for readers. Traditional research can be difficult to consume, so Distill communicates ML research in appealing, interactive data visualizations. It acts as a neutral platform for multiple authors to publish together, and articles are peer-reviewed, appearing in Google Scholar. Distill is also registered with the Library of Congress and CrossRef.
3. Machine Learning is Fun
Machine Learning is Fun is a valuable, introductory blog. It covers the tenets of ML through interactive tutorials and practical examples, which make it easier to see the useful applications to different businesses and industries. Author Adam Geitgey is a former software developer who now consults organizations on implementing machine learning. He believes ML is integral to the future of software and that developers should have a strong working knowledge, so he provides guides and techniques to help them develop and grow.
4. Machine Learning Mastery
A machine learning developer with several AI-related degrees, Jason intended his Machine Learning Mastery blog for new developers getting started in the field. He was once an amateur developer and wants to help others, imparting lessons learned during his professional journey and sharing the tools that helped him most. The blog, plus his email course and newsletter, accommodate any level of expertise.
5. The BAIR Blog
The artificial intelligence research department at UC Berkeley created this blog to convey research findings and important information about their AI-related work. Covering a spectrum of research—from natural language processing to robotics—grad students and faculty contribute content for both experts and the general population to consume.
6. FastML
FastML tackles interesting topics in machine learning with entertaining, easy to consume posts. It’s run by economist Zygmunt Zając, and is a go-to ML platform, tackling topics like overfitting, pointer networks, and chatbots, among others. If you’re frustrated by some of the existing ML papers that feel like you need a PhD in math to understand them, bookmark this blog.
7. AI Trends
This media channel delivers comprehensive coverage of the latest AI-related technology and business news. It’s designed to keep executives ahead of the curve with artificial intelligence and machine learning. AI Trends features interviews with and thought leadership pieces from top business leaders, as well as in-depth articles on the business of AI.
8. AWS Machine Learning Blog
Amazon is heavily involved in ML, using algorithms in nearly all areas of its business to create leads. Algorithms suggest relevant products for customers in search results, recommend products based on recent purchases, and optimize faster product distribution and shipping from warehouses to customers. The blog features projects and guides that reveal industry strides to readers and covers ML uses in Amazon Web Services technology.
9. Apple Machine Learning Journal
Apple’s advancements in voice recognition, predictive text, and autocorrect leveraged for Siri signal some of its machine learning work. And their newest iPhone features ML predominantly in its processor, performing trillions of operations per second; it’s ML in your hands. Apple Machine Learning Journal is a helpful look at how ML shapes their different technologies, and Apple engineers give perspective on how their work influences the transformation of ML.
10. AI at Google
Google helped revolutionize machine learning, so to see their level of ML research isn’t surprising. Machine learning and AI critically support how Google technology works—from their search algorithms that redefined web searches, to Google Maps influencing how we navigate destinations, and now their self-driving car is changing the auto industry. Google makes its work available through posts discussing their published research and how its technology is used by others to influence AI innovation.
With greater understanding or appreciation for ML and AI, it’s easier to dispel the myths that may leave doubt about their full potential and to responsibly apply these productive solutions. A Tableau blog post recently explored three common machine learning misconceptions—reviewing them will help you discern fact from fiction in all the industry noise.
For anyone ready to embrace these models and put them to work, Andrew Beers, CTO at Tableau Software, wrote about how to effectively and responsibly apply AI techniques taking cues from brands such as Box, eBay, OpenTable, and Slack. And the rise of explainable AI (i.e. techniques in AI which can be trusted and easily understood) topped Tableau’s 2019 annual report of influential BI trends, signaling that more organizations are putting these trained, data-driven models to use in how they operate and solve complex challenges.
[“source=tableau”]
