Pretty much every presentation I give about AI includes the following slide, in the context of automatically authenticating users using face recognition. The idea is that even automation-reliant businesses don’t need to go 100% automatic to start serving customers: you can start with a basic model that outputs a confident “yes” (green region), a confident “no” (red region)
It’s common to see job descriptions along the lines of the following. -------------------------------------------------------------------------------- Looking for a data scientist. Responsibilities: 1. Building big data databases, data warehouses, data lakes, and pipelines. 2. Training and deploying deep learning models and neural networks for prediction. 3. Creating dashboards, analyses, visualisationgs and reports. 4. Supporting marketing and other teams with A/B test analyses,
As the popularity of machine learning grows, so does the amount of academic papers, blog posts and software released. As an industry practicioner I need to be up-to-date with useful ones without spending hours each week combing through new developments to find the few important updates. The obvious solution to this is curation: having people or algorithms digest
There’s a specific type of person I’ve noticed applying to jobs, participating in ML communities, or writing to me online: software engineers with an interest in machine learning. Their ML background typically consists of a course or two from Andrew Ng [https://www.coursera.org/courses?query=machine%20learning%20andrew%20ng], and perhaps a few other MOOCs. This
Ask them if AI will kill all humans. Actually, anything along these lines will work. When will we achieve 100% automation? When are we going to build a machine that is better than humans at every kind of work? These are questions I often get at talks, interviews, or one-on-one conversations. Others associating with AI in their work
It’s often assumed you need a number of PhDs and double the number of developers to create useful machine learning (ML) solutions. This is only sometimes true. If you’re creating leading-edge products — meaning you’re developing brand new machine learning methods — then you will certainly need a team of highly skilled and quite expensive talent. However, most
I’ve often needed to enter a domain where I previously had no expertise: e.g. when I started my blog, when I decided to consciously work on my social skills, or when I decided I want to understand business better. Getting an initial understanding of the new domain is really hard because there are many competing frameworks and schools
I’m reading a book on happiness and meditation which contains the following story. In Chinese history, during the reign of Emperor Shun (who lived sometime around 2200 BCE), what was then known as China was plagued by frequent destructive floods along the Yellow River. The emperor ordered a nobleman called Gun to solve the problem. Gun’s strategy was
