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) …
While many of the programming libraries encapsulate the inner working details of graph and other algorithms, as a data scientist it helps a lot having a reasonably good familiarity of such details. A solid understanding of the intuition behind such algorithms not only helps in appreciating the logic behind them but also helps in making conscious decisions about their applicability in real life cases. There are several graph based algorithms and most notable are the shortest path algorithms. Algorithms such as Dijkstra's, Bellman Ford, A*, Floyd-Warshall and Johnson's algorithms are commonly encountered. While these algorithms are discussed in many text books and informative resources online, I felt that not many provided visual examples that would otherwise illustrate the processing steps to sufficient granularity enabling easy understanding of the working details.
Deriving more value from analytics and emerging technologies like artificial intelligence starts with trust, simply because data collected for analytics must be trusted. Customers and partners that share data must trust that it's safeguarded and used appropriately from collection through storage and to how it's applied. And once insights emerge from applying analytics to the data, individuals throughout the organization must understand the care given to data management so that they trust those insights -- and use them -- to make decisions and ask new questions. Our global survey of more than 2,400 business leaders and managers provides insight into organizations' activities in each of these key areas and identifies where recognized best practices are becoming more mainstream and where they may still be exceptional. It found that respondents who have advanced their analytics practices to incorporate AI-based technologies such as machine learning and natural language processing work in organizations that do the most to foster data quality, safeguard data assets, and develop cultures of data literacy and innovation.
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Pull stock prices from online API and perform predictions using Recurrent Neural Network & Long Short Term Memory (LSTM) with TensorFlow.js framework Machine learning is becoming increasingly popular these days and a growing number of the world's population see it is as a magic crystal ball: predicting when and what will happen in the future. This experiment uses artificial neural networks to reveal stock market trends and demonstrates the ability of time series forecasting to predict future stock prices based on past historical data. Disclaimer: As stock markets fluctuation are dynamic and unpredictable owing to multiple factors, this experiment is 100% educational and by no means a trading prediction tool. Before we can train the neural network and make any predictions, we will first require data. The type of data we are looking for is time series: a sequence of numbers in chronological order.
Yes, companies use AI to automate various tasks, while consumers use AI to make their daily routines easier. But governments–and in particular militaries–also have a massive interest in the speed and scale offered by AI. Nation states are already using artificial intelligence to monitor their own citizens, and as the U.K.'s Ministry of Defence (MoD) revealed last week, they'll also be using AI to make decisions related to national security and warfare. The MoD's Defence and Security Accelerator (DASA) has announced the initial injection of £4 million in funding for new projects and startups exploring how to use AI in the context of the British Navy. In particular, the DASA is looking to support AI- and machine learning-based technology that will "revolutionise the way warships make decisions and process thousands of strands of intelligence and data."
Sign up here to receive the Davos Diary, a special daily newsletter that will run from Jan. 20-24. IBM called for rules aimed at eliminating bias in artificial intelligence to ease concerns that the technology relies on data that bakes in past discriminatory practices and could harm women, minorities, the disabled, older Americans and others. As it seeks to define a growing debate in the U.S. and Europe over how to regulate the burgeoning industry, IBM urged industry and governments to jointly develop standards to measure and combat potential discrimination. The Armonk, New York-based company issued policy proposals Tuesday ahead of a Wednesday panel on AI to be led by Chief Executive Officer Ginni Rometty on the sidelines of the World Economic Forum in Davos. The initiative is designed to find a consensus on rules that may be stricter than what industry alone might produce, but that are less stringent than what governments might impose on their own.
MolenGeek started in 2015 in Molenbeek, Belgium, as a coding school for anyone to learn digital skills. But unlike many other schools, MolenGeek is driven by a social mission of fostering inclusion, integration and community development in this culturally diverse suburb of Brussels. In five years, it's become a co-working space for young people from all backgrounds, enabling them to network and share their experiences. Out of Molengeek's community of 800 active members, 195 people from predominantly underprivileged backgrounds have gone through entrepreneurship skills training, and 35 new startups have been built and grown out of their incubator program. Sundar Pichai, CEO of Google and Alphabet, visited MolenGeek to announce an additional Google.org
AI has fallen from glorious summers into dismal "winters" before. The temptation to predict another such tumble recurs naturally. So that is the question the BBC posed to AI researchers: Are we on the cusp of an "AI winter": The 10s were arguably the hottest AI summer on record with tech giants repeatedly touting AI's abilities. AI pioneer Yoshua Bengio, sometimes called one of the "godfathers of AI", told the BBC that AI's abilities were somewhat overhyped in the 10s by certain companies with an interest in doing so. There are signs, however, that the hype might be about to start cooling off.
The surface area for testing software has never been so broad. Applications today interact with other applications through APIs, they leverage legacy systems, and they grow in complexity from one day to the next in a nonlinear fashion. What does that mean for testers? The 2016-17 World Quality Report suggests that AI will help. "We believe that the most important solution to overcome increasing QA and Testing Challenges will be the emerging introduction of machine-based intelligence," the report states.