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) …
When we don't experience immediate success -- in any task, not just data science -- we have three options: While option three is the best choice on an individual and community level, it takes the most courage to implement. I can selectively choose ranges when my model delivers a handsome profit, or I can throw it away and pretend I never spent hours working on it. We advance by repeatedly failing and learning rather than by only promoting our success. Moreover, Python code written for a difficult task is not Python code written in vain! This post documents the prediction capabilities of Stocker, the "stock explorer" tool I developed in Python.
The Japanese investment trust market is at a turning point as popular monthly-distribution trusts have begun to see a net outflow of funds while artificial intelligence- and big data-oriented funds are attracting investors expecting high growth. Long-term investment trusts are also in a firm state in line with a growing trend toward asset-building and away from savings in Japan. In 2017, open-type investment trusts, excluding exchange-traded funds, witnessed a new inflow of funds totaling 2.71 trillion yen ($24.34 billion). With their outstanding balance continuing to increase, investment trusts are taking hold as a key asset-building tool for individuals. According to data compiled by QUICK Asset Management Research Center on fund flows for investment trust management companies, individual investors are being lured to trusts that focus on AI and other cutting-edge technologies.
The new Business Intelligence Lab (BIL) will focus on effective and efficient data analysis technology for emerging data intensive applications, while the Robotics and Autonomous Driving Lab (RAL) will concentrate on computer vision, particularly in autonomous driving in order to solidify Baidu's base technologies in this field. The new labs will bring Baidu Research's presence to a total of five labs, adding to the existing Institute of Deep Learning (IDL), Big Data Lab (BDL) and Silicon Valley Artificial Intelligence Lab (SVAIL). Together, the labs will continue to focus on fundamental research in their specialized areas to work toward the long-term innovation of fundamental AI technologies. They will also work with corresponding Baidu AI technology departments to drive Baidu's AI development and accelerate commercialization.
The data researchers no longer depend only on interviews, surveys, observational studies to collect data. Instead, they have switched to the faster ways of data collection which includes leveraging internet, cameras, smartphones, drones, bots and many more. Later, the collected data is used by organization / governments to make business decisions. But, before that, they require a device or system which can store and secure such big data sets. One such system is Hadoop File Distribution System, commonly known as HDFS.
There's an awful lot of text data available today, and enormous amounts of it are being created on a daily basis, ranging from structured to semi-structured to fully unstructured. What can we do with it? Well, quite a bit, actually; it depends on what your objectives are, but there are 2 intricately related yet differentiated umbrellas of tasks which can be exploited in order to leverage the availability of all of this data. NLP is a major aspect of computational linguistics, and also falls within the realms of computer science and artificial intelligence. Text mining exists in a similar realm as NLP, in that it is concerned with identifying interesting, non-trivial patterns in textual data.
At KDnuggets, we try to keep our finger on the pulse of main events and developments in industry, academia, and technology. We also do our best to look forward to key trends on the horizon. To close out 2017, we recently asked some of the leading experts in Big Data, Data Science, Artificial Intelligence, and Machine Learning for their opinion on the most important developments of 2017 and key trends they 2018. This post, the first in this series of such year-end wrap-ups, considers what happened in Big Data this year, and what may be on the horizon for 2018. "What were the main Big Data related developments in 2017, and what key trends do you see in 2018?"
Almost a quarter century ago, a book was written about how organizations would focus on share of customer as opposed to share of market, building a personalized collaboration driven by big data. With advanced analytics, banking may finally getting close to realizing this vision. In 1993, a then revolutionary book, "The One to One Future: Building Relationships One Customer at a Time" was published, proposing the idea that as technology makes it affordable to track individual customers, marketing shifts from finding customers for products to finding products for customers. According to the authors, Don Peppers and Martha Rogers, Ph.D., a company could use technology to gather information about, and to communicate directly with, individuals to form a commercial bond. The book became a bestseller, and was on every marketer's bookshelf … almost a quarter century ago.
A recently released market research report shows the market for machine learning growing at a rapid 44.1% compounded annual growth rate over the next five years, driven largely by the financial services sector, where big data can yield critical and actionable business insights. In the world of behavioral biometrics, machine learning, deep learning and artificial intelligence are all hand-in-glove. Behavioral biometrics identifies people by how they interact with devices and online applications. As opposed to something that someone has like a device, token or a static attribute like a fingerprint or a name, behavioral biometrics is a dynamic modality that is completely passive and works in the background, making it impossible to copy or steal. Today's behavioral biometric technologies can capture more than 2,000 parameters from a mobile device, including the way a person holds the phone, scrolls, toggles between fields, the pressure they use when they type and how they respond to different stimuli that are presented in online applications.
Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ... Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... Microsoft's Latest AI Creation Reveals Just How Much Computers Can Imagine today Google announces scholarship program to train 1.3 lakh Indian developers in emerging technologies 43890 views Want to be a millionaire before you turn 25? Study artificial intelligence or machine learning 43215 views