When you think of artificial intelligence (AI), do you envision C-3PO or matrix multiplication? HAL 9000 or pruning decision trees? This is an example of ambiguous language, and for a field which has gained so much traction in recent years, it’s particularly important that we think about and define what we mean by artificial intelligence – especially when communicating between managers, salespeople, and the technical side of things. These days, AI is often used as a synonym for deep learning, perhaps because both ideas entered popular tech-consciousness at the same time. In this article I’ll go over the big picture definition of AI and how it differs from machine learning and deep learning. Continue reading
At The Data Incubator we run a free eight-week data science fellowship to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring data scientists. Michael was a Fellow in our Winter 2016 cohort who landed a job with one of our hiring partners, Schireson Associates.
My PhD work was in computational materials science, where I worked with reactive molecular dynamics simulations. The field is totally simulation based, and typically requires high performance computing resources. Running these simulations helped build my chops for working with parallel systems and command line tools. The software required familiarity with some powerful languages and APIs like C and CUDA. Learning those definitely helped my understanding of Python once I converted to using it.
Toward the third year of my PhD I got really interested in machine learning. I started using scikit-learn to predict different aspects of simulations I worked on. These projects became a large part of my thesis and contributed to choosing The Data Incubator as a next step in my career.