In my undergraduate studies, I majored in philosophy with a focus on ethics, spending countless hours grappling with the notion of fairness: both how to define it and how to effect it in society. Little did I know then how critical these studies would be to my current work on the machine learning education team where I support efforts related to the responsible development and use of AI.
As ML practitioners build, evaluate, and deploy machine learning models, they should keep fairness considerations (such as how different demographics of people will be affected by a model’s predictions) in the forefront of their minds. Additionally, they should proactively develop strategies to identify and ameliorate the effects of algorithmic bias.
Informational keyword research is a subject that has been covered thousands of times across every SEO blog, publication, and web design company.
However, with voice becoming a more prominent way of searching, it’s important that it’s now taken into consideration. With voice usage growing, marketers need to understand how their audience is using this technology, and how they can adapt to this. Keyword research has been advancing dramatically over the last couple of years. No longer are the days of simply sorting by the highest search volume and creating a page; it comes down to much more than that. Semantics, categorisation, ranking difficulty vs reward, questions, featured snippets, people also ask. The list goes on.
Social proof plays a major role in your customers’ purchase decisions. Customers book hotels based on the reviews they read, they try new restaurants based on how many people they see inside, and they buy products based on the testimonials they’ve read. Even when people are impressed by your products and services, they almost always Read more