Information Technology: Overviews



Top Emerging Trends In 2018 For The Supply Chain

#artificialintelligence

The last five years have been the, "coming of age," period for technologies like the Internet of Things (IoT), machine learning, mixed reality (MR), and blockchain. By late 2017, these technologies gained enough maturity and stability for use in industrial settings. As such, 2018 is shaping-up to be...


Three FAQs About Preparing for AI in the Workplace

#artificialintelligence

Artificial intelligence (AI) is making inroads in just about every job that involves data processing, repetition, or predictive thinking. Instead of worrying about being replaced by AI, it's time to get ready for when AI will arrive in your workplace. Here are some of the most frequently asked quest...


Poker AI Revolution [Infographic] Techno FAQ

#artificialintelligence

AI has a long history of defeating human players in games. IBM's "Deep Blue" developed by Carnegie Mellon University beat chess world champion Garry Kasparov in their re-match in 1997. Google AlphaGo AI won the game "Go" by defeating leading Go player Lee Sedol. IBM supercomputer Watson beat two "Je...


Make Artificial Intelligence in India, Make Artificial Intelligence Work for India: PM Modi

#artificialintelligence

Ladies and Gentlemen, I am happy to be here today at the inauguration of the Wadhwani Institute of Artificial Intelligence. Let me begin by congratulating Romesh Wadhwani ji and Sunil Wadhwani ji,The Government of Maharashtra, and Mumbai University for coming together to make this Institute a reali...


Journal of Biometrics and Biostatistics - Open Access Journals

#artificialintelligence

Biometrics and Biostatistics are disciplines of biological sciences concerned with the application of mathematical-statistical theory, principles, and practices to the observation, measurement, and analysis of biological data and phenomena. Biostatistics deals with the application of statistics to a...


A Guide to AWS

@machinelearnbot

Even those new to IT have probably heard that everyone is "moving to the cloud." This transition from standard infrastructure is thanks in large part to Amazon Web Services. Currently, AWS offers "over 90 fully featured services for computing, storage, networking, analytics, application services, d...


How AI & Chatbots Are Changing Education Worldwide

#artificialintelligence

Is the FHE teaching capability and capacity improving as fast as it should? Some of our knowledge about teaching and learning go back to Greek times and still hold true. But that is not to say that more recent research and technology should be ignored. It is generally accepted that Moore's Law is ...


Who Killed Albert Einstein? From Open Data to Murder Mystery Games

arXiv.org Artificial Intelligence

This paper presents a framework for generating adventure games from open data. Focusing on the murder mystery type of adventure games, the generator is able to transform open data from Wikipedia articles, OpenStreetMap and images from Wikimedia Commons into WikiMysteries. Every WikiMystery game revolves around the murder of a person with a Wikipedia article and populates the game with suspects who must be arrested by the player if guilty of the murder or absolved if innocent. Starting from only one person as the victim, an extensive generative pipeline finds suspects, their alibis, and paths connecting them from open data, transforms open data into cities, buildings, non-player characters, locks and keys and dialog options. The paper describes in detail each generative step, provides a specific playthrough of one WikiMystery where Albert Einstein is murdered, and evaluates the outcomes of games generated for the 100 most influential people of the 20th century.


Isolating Sources of Disentanglement in Variational Autoencoders

arXiv.org Artificial Intelligence

We decompose the evidence lower bound to show the existence of a term measuring the total correlation between latent variables. We use this to motivate our $\beta$-TCVAE (Total Correlation Variational Autoencoder), a refinement of the state-of-the-art $\beta$-VAE objective for learning disentangled representations, requiring no additional hyperparameters during training. We further propose a principled classifier-free measure of disentanglement called the mutual information gap (MIG). We perform extensive quantitative and qualitative experiments, in both restricted and non-restricted settings, and show a strong relation between total correlation and disentanglement, when the latent variables model is trained using our framework.