If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Facial recognition is about to become an increasingly common security measure at US airports. The Transportation Security Administration has published a roadmap for how it plans to integrate new biometric data systems into airports across the country. This includes plans to use fingerprints and facial scans at airport checkpoints, potentially leading to shorter lines. It could also mean travelers may be able to leave their passports at home in the future. Facial recognition is about to become more common at US airports.
Buzzwords like "deep learning" and "neural networks" are everywhere, but so much of the popular understanding is misguided, says Terrence Sejnowski, a computational neuroscientist at the Salk Institute for Biological Studies. Sejnowski, a pioneer in the study of learning algorithms, is the author of The Deep Learning Revolution (out next week from MIT Press). He argues that the hype about killer AI or robots making us obsolete ignores exciting possibilities happening in the fields of computer science and neuroscience, and what can happen when artificial intelligence meets human intelligence. The Verge spoke to Sejnkowski about how "deep learning" suddenly became everywhere, what it can and cannot do, and the problem of hype. This interview has been lightly edited for clarity.
If you're looking for an answer to any of the above questions, then this blog series should really help you get started. So, what is Exploratory Data Analysis (EDA)? Exploratory data analysis (EDA) is a crucial component of data science which allows you to develop the gist of what your data look like and what kinds of questions might be answered by them. Ultimately, EDA is important because it allows the investigator to make critical decisions about what is interesting to pursue and what probably isn't worth following up on and thus building a hypothesis using the relationships between variables. This is a two part series where we are going to look into a Movie dataset from Kaggle and we'll do some exploratory analysis to investigate the data.
An understanding of the alphabet soup of current and future payment models will be needed -- and it must begin with the current fee-for-service (FFS) model. In this system, specific medical services, procedures, and supplies are reimbursed using the CMS Healthcare Common Procedure Coding System (HCPCS)1. Level I of the HCPCS system is based on Current Procedural TerminologyTM (CPT), which is a numeric coding system developed and maintained by the American Medical Association.The CPT system identifies and describes medical services and procedures commonly furnished and billed by physicians and other healthcare professionals. However, CPT does not include the codes needed to separately report medical items or services for patients that are provided outside of the physician office setting (e.g., durable medical equipment and supplies). The Level II HCPCS was established to provide codes for non-physician providers to submit claims for these items to Medicare and private health insurance programs.