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) …
We've got lots of data in Loss Prevention (LP): months of video stored somewhere in our infrastructure and multiple systems that house financial data. For years, we've described the need for a data pipeline to consolidate all that data into one place to better understand our business. The data pipeline implementation takes the raw data in an operation and stores it in a data lake. From the data lake, one can join all this disparate data and video together in multiple ways to derive more insight from these assets. Let's say an LP staff member finds a new exception scenario that is causing your organization to lose money.
It's not who has the best algorithm that wins; It's who has the most data -- Andrew Ng. Image classification is the task of assigning an input image one label from a fixed set of categories. This is one of the core problems in Computer Vision that, despite its simplicity, has a large variety of practical applications. In this blog I will be demonstrating how deep learning can be applied even if we don't have enough data. I have created my own custom car vs bus classifier with 100 images of each category.
This is a continuation of our series on machine learning methods that have been implemented in JASP (version 0.11 onwards). In this blog post we train a machine learning model to find clusters within our data set. The goal of a clustering task is to detect structures in the data. To do so, the algorithm needs to (1) identify the number of structures/groups in the data, and (2) figure out how the features are distributed in each group. For instance, clustering can be used to detect subgenres in electronic music, subgroups in a customer database, or to identify areas where there are greater incidences of particular types of crime.
Our personalities impact almost everything we do, from the career path we choose to the way we interact with others to how we spend our free time. But what about the way we drive--could personality be used to predict whether a driver will cut someone off, speed, or, say, zoom through a yellow light instead of braking? There must be something to the idea that those of us who are more mild-mannered are likely to drive a little differently than the more assertive among us. At least, that's what a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) is betting on. "Working with and around humans means figuring out their intentions to better understand their behavior," said graduate student Wilko Schwarting, lead author on the paper published this week in Proceedings of the National Academy of Sciences.
Nowadays, network security is a business cornerstone of Internet Service Providers (ISPs), who must cope with an increasing number of network attacks, which put the integrity of the entire network at risk. Current network monitoring systems provide data with a high degree of dimensionality. This opens the door to the large-scale application of machine learning approaches to improve the detection and classification of network attacks. In recent years, the use of machine learning based systems in network security applications has gained in popularity. Such use usually consists of incorporating traditional (and shallow) machine learning models, for which a set of expertly handcrafted features is required to pre-process the data prior to training the models.
Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I'd like to step back and explain both machine learning and deep learning in basic terms, discuss some of the most common machine learning algorithms, and explain how those algorithms relate to the other pieces of the puzzle of creating predictive models from historical data. Recall that machine learning is a class of methods for automatically creating models from data. Machine learning algorithms are the engines of machine learning, meaning it is the algorithms that turn a data set into a model. Which kind of algorithm works best (supervised, unsupervised, classification, regression, etc.) depends on the kind of problem you're solving, the computing resources available, and the nature of the data.
Data science or data-driven science enables better decision making, predictive analysis, and pattern discovery. In practice, data science is already helping the airline industry predict disruptions in travel to alleviate the pain for both airlines and passengers. In another example, let's say you want to buy new furniture for your office. When looking online for the best option and deal, you should answer some critical questions before making your decision. Using this sample decision tree, you can narrow down your selection to a few websites and, ultimately, make a more informed final decision.
I am sure by now you must have heard about this term, and if surprisingly not, just have a look at the presentations by Apple or Google; even McDonald's is doing something. In fact not only them, if you were to look at the Google Trends page for Machine Learning, you'll notice the upward trend in popularity and interest over time. You can easily find many popular use-cases of Machine Learning. I am sure you check Amazon for when you need to buy new clothes or shoes. And then you see a list of recommended items for you.
AWS's Machine Learning includes three techniques, binary classification, multiclass classification, and regression. What we will do in this course is to look at these three machine learning techniques with three different data sets. To keep things interesting, we will use Kaggle's data sets for two of our examples. If you are new to machine learning, don't worry, you'll learn machine learning concepts along the way and I'll walk you through the AWS console. We will work our way through the six Amazon Machine Learning steps.