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) …
This would limit me to predicting changes one district at a time. I'm still in the planning stage of this homework assignment, but before I went too far down the HMM track I wanted to see if I'm barking up the right tree. I want to predict the number of Ebola cases by geographical district, over time. I have a data set which tracks new confirmed Ebola cases across 20 districts, through 100 weeks. This data is in the form of discrete integers representing the number of confirmed new cases.
I am happy to announce that Prelert and Elastic are joining forces. Ever since we started Elastic, our goal has been to allow users to easily find relevant data or insights within large amounts of data. Search is a wonderful way to do it, and the ability to slice, dice, and aggregate the data in an unconstrained way allowed users to feel they are in control of the data, compared to the other way around. But we can take it a step forward, and with Prelert, we just did. Prelert has developed an unsupervised machine learning engine that can plow through large amounts of data and automatically find those insights our users today have been proactively finding using search.
After suffering its first defeat in the Google DeepMind Challenge Match on Sunday, the Go-playing AI AlphaGo has beaten world-class player Lee Se-dol for a fourth time to win the five-game series 4-1 overall. The final game proved to be a close one, with both sides fighting hard and going deep into overtime. AlphaGo is an AI developed by Google-owned British company DeepMind, and had already wrapped up a historic victory on Saturday by becoming the first ever computer program to beat a top-level Go player. The win came after a "bad mistake" made early in the game, according to DeepMind founder Demis Hassabis, leaving AlphaGo "trying hard to claw it back." By winning the final game despite its blip in the fourth, AlphaGo has demonstrated beyond doubt its superiority over one of the world's best Go players, reaffirming a major milestone for artificial intelligence in the process.
Tech giants like Facebook, Google, and Baidu know that people aren't filling their devices with apps anymore. Just 35 percent of smartphone users download a single app in an average month, and the average app loses 90 percent of its daily active users within 30 days of release. While it might be fun to slice fruit or slingshot cartoon birds while waiting for the bus, these apps can't offer the frictionless experience users crave. Consumers want a new, on-demand kind of app: one clad in a conversational interface, ready to serve, and capable of complex actions. Want to check your flight status, book an Uber for when you land, and schedule your meetings for that afternoon?
Ben Robinson, chief strategy & marketing officer at banking software firm Temenos, explains how the future of finance will be shaped by artificial technology. Google's DeepMind triumph this year over one of the world's highest ranked champions at Go is a sign: computers with artificial intelligence (AI) are learning how to outperform us. If computers can learn to beat us at a game, why not at things that we can't afford to get wrong – medical diagnostics, risk analysis, legal and investment advice? And what affect over time – say 20 years – will this have on the way a host of services, such as banking, are delivered? "Service will be all about data and algorithms," David Brear, co-founder and CEO at 11:FS recently suggested.
There's another round of funding for an artificial intelligence startup in Asia that's focused on e-commerce. Fresh from a Sequoia-led investment in Mad Street Den last month, now Singapore-based ViSenze has announced a 10.5 million Series B raise. Existing investor Rakuten Ventures led the round alongside WI Harper Group and Enspire Capital. A number of other investors also participated, including SPH Media Fund, FengHe Fund Management, Raffles Venture Partners, Phillip Private Equity, and UOB Venture Management. The startup was spun out of the National University of Singapore more than three years ago and it raised a 3.5 million Series A in February 2014.
Tesla and former pal Mobileye aren't quite done airing controversial statements against each other. After the company behind Autopilot's image recognition hardware said it severed its ties with the automaker because it was "pushing the envelope in terms of safety," Tesla fired back with its own feisty response. A company spokesperson told Reuters that Mobileye wasn't happy when it learned that Tesla decided to work on its own vision chips for Autopilot. She said Mobileye "attempted to force Tesla to discontinue this development, pay them more and use their products in future hardware." Mobileye and Tesla parted ways following the fatal Model S crash in Florida that put the carmaker's Autopilot feature in hot water.
Microsoft's Office Lens app has let folks upload photos from their iOS and Android devices since April 2015. With integration in the latest Windows OS, though, they've also added support for Office 365 if your business or personal accounts use that instead. It's still got all the optical character recognition you've enjoyed with OneNote since 2013, meaning the text and figures are searchable once you've uploaded the images to OneDrive.
We investigate the use of machine learning techniques into building statistically stable systematic allocation strategies. Traditionally, allocation processes usually rely on variations of Markowitz framework such as Mean Variance allocation, Maximum Diversity, Risk Parity, Conditional Value at Risk, ie convex frontier optimization. Although those methods show some efficiency to allocate assets through the convex efficient frontier, they usually rely deeply on the estimation and the usage of the covariance matrix. Being no stationary and having multiple range memory (ie FIGARCH), the statistical estimation of covariance may lead to biases and errors and in the end, bias conclusions. Very extensive literature in econo-metrics, econo-physics, quantitative allocation cover this problem in order to remedy to the statistical estimation of covariance and his bias and issues.