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) …
Dear Makers, We are hosting our fifth meetup on 27th November. Talk 1: Scalable Automatic Machine Learning in H2O by Erin LeDell The focus of this presentation is scalable and automatic machine learning using the H2O machine learning platform. H2O is an open source, distributed machine learning platform designed for big data. The core machine learning algorithms of H2O are implemented in high-performance Java, however, fully-featured APIs are available in R, Python, Scala, REST/JSON, and also through a web interface. Since H2O's algorithm implementations are distributed, this allows the software to scale to very large datasets that may not fit into RAM on a single machine.
If you've been in Data Engineering, or what we once referred to as Business Intelligence, for more than a few years you've probably spent time building an ETL process. With the advent of (relatively) cheap storage and processing power in data warehouses, the majority of bulk data processing today is designed as ELT instead. Though this post speaks specifically to Amazon Redshift, most of the content is relevant to other similar data warehouse architectures such as Azure SQL Data Warehouse, Snowflake and Google BigQuery. First, ETL stands for "Extract-Transform-Load", while ELT just switches to order to "Extract-Load-Transform". Both are approaches to batch data processing used to feed data to a data warehouse and make it useful to analysts and reporting tools.
The Apache Software Foundation (ASF) recently announced that SINGA, a framework for distributed deep-learning, has graduated to top-level project (TLP) status, signifying the project's maturity and stability. SINGA has already been adopted by companies in several sectors, including banking and healthcare. Originally developed at the National University of Singapore, SINGA joined ASF's incubator in March 2015. SINGA provides a framework for distributing the work of training deep-learning models across a cluster of machines, in order to reduce the time needed to train the model. In addition to its use as a platform for academic research, SINGA has been used in commercial applications by Citigroup and CBRE, as well as in several health-care applications, including an app to aid patients with pre-diabetes.
We are now in the Fourth Industrial Revolution. Artificial Intelligence (AI) is the fuel behind all the developments that we are witnessing in this era. The continuous and vast development of computing infrastructure changed our goal from machine programming to machine learning. Today we see self-driving cars, translation software, virtual assistants, drones, and other things which are powered by AI. As our technologies continue to grow, AI will dominate our cities even further.
Feature selection is a crucial preprocessing step in data analytics and machine learning. Classical feature selection algorithms select features based on the correlations between predictive features and the class variable and do not attempt to capture causal relationships between them. It has been shown that the knowledge about the causal relationships between features and the class variable has potential benefits for building interpretable and robust prediction models, since causal relationships imply the underlying mechanism of a system. Consequently, causality-based feature selection has gradually attracted greater attentions and many algorithms have been proposed. In this paper, we present a comprehensive review of recent advances in causality-based feature selection.
Deep learning has proved its supremacy in the world of supervised learning, where we clearly define the tasks that need to be accomplished. But, when it comes to unsupervised learning, research using deep learning has either stalled or not even gotten off the ground! There are a few areas of intelligence which our brain executes flawlessly, but we still do not understand how it does so. Because we don't have an answer to the "how", we have not made a lot of progress in these areas. If you liked my previous article on the functioning of the human brain to create machine learning algorithms that solve complex real world problems, you will enjoy this introductory article on Hierarchical Temporal Memory (HTM). I believe this is the closest we have reached to replicating the underlying principles of the human brain. In this article, we will first look at the areas where deep learning is yet to penetrate.
Ever since computers were invented, there has been an exponential growth in their ability and potential to perform various tasks. In order to use computers across diverse working domains, humans have developed computer systems while increasing their speed, and reducing size with respect to time. Artificial Intelligence pursues the stream of developing the computers or machines to be as intelligent as humans themselves. In this article we will scrape the top layer about the concepts of artificial intelligence that will help understand related concepts like Artificial Neural Networks, Natural Language Processing, Machine Learning, Deep Learning, Genetic algorithms etc. Along with this, we will also learn about its implementation in Python.
It's an exciting time to be investing in mobility startups. Below are the current trends in the mobility industry and an overview of the startup ecosystem in Europe. The mobility industry is undergoing rapid change these days. While they bring opportunities for newcomers, they create challenges for the incumbents. Let's have a look at the four trends in more detail: While Mercedes Benz had already started to experiment with self-driving technology three decades ago, it wasn't until recently that autonomous driving (AD) efforts really speed up.
We all have a camera in our pockets, and nearly anyone can live video chat with minimal barrier to entry. Today, the ability to connect to anyone in the world through video is easier than ever -- and it will only get easier as technology advances. One emerging trend in the video space is live-streaming, or streaming video simultaneously captured and broadcast in real time. But live video has a dangerous downside: the lack of control. There's no "oops" button when live-streaming -- anything can happen, and it's difficult to contain what happens to the content.
Do you default to primary research to gather qualitative insights? That may not always be necessary. Increasingly, cutting edge machine learning algorithms mine existing data for rich qualitative insights that can be used to inform new product development and improve marketing messaging. This webinar will provide an overview of how machine learning can be used to uncover actionable insights quickly and cost-effectively.