San Francisco (AFP) - Mark Zuckerberg envisions a software system inspired by the "Iron Man" character Jarvis as a virtual butler managing his household. The Facebook founder's dream is about artificial intelligence, which is slowly but surely creeping into our daily lives, no longer just science fiction. Artificial intelligence or AI is getting a foothold in people's homes, starting with the Amazon devices like its Echo speaker which links to a personal assistant "Alexa" to answer questions and control connected devices such as appliances or light bulbs. Analyst Carolina Milanesi of the research firm Creative Strategies said that "2016 was the year about raising awareness, and exposing consumers to the idea of AI in a more mass market way." Milanesi said it may take time for the technology to fulfill its potential, noting that companies need "a strong hook" to bring large numbers of consumers into this world.

Technology is really a marvel. So often, as new advances come out that allow us to do things that were thought impossible in the past, we hear the phrase "The future is now." Popular Science and XPRIZE are teaming up to explore and explain technologies like these in a video series called Future First. Episode thirteen of Future First is titled "Brain Hacking With Video Games." In it, we take a look at how new technologies--including virtual reality--help us to track activity within the human brain in real time, how different experiences affect different parts of the brain, and how we can use that information to further our knowledge of the mind.

Although investors scrambled--and shelled out up to $185,000 a pop--for the chance to snatch up the new domains and profit as gatekeepers, uptake among end-users has been underwhelming. More than three years after the program's launch, roughly 26 million new generic top-level domains have been registered, compared with the 164 million registered "legacy" top-level domains. Cyrus Namazi, the vice president of domain-name services and industry engagement at ICANN, acknowledged that demand for new top-level domains won't eclipse that for legacies "any time soon." Yet Namazi believes registrations for the new extensions will continue to grow. "We are in the embryonic stages of the expansion," he said.

Among the many R packages, there is the outbreaks package. It contains datasets on epidemics, on of which is from the 2013 outbreak of influenza A H7N9 in China, as analysed by Kucharski et al. (2014): I will be using their data as an example to test whether we can use Machine Learning algorithms for predicting disease outcome. To do so, I selected and extracted features from the raw data, including age, days between onset and outcome, gender, whether the patients were hospitalised, etc. Missing values were imputed and different model algorithms were used to predict outcome (death or recovery). The thus prepared dataset was devided into training and testing subsets. The test subset contained all cases with an unknown outcome. Before I applied the models to the test data, I further split the training data into validation subsets.

Google Open Sources Data Visualization Tool for Machine Learning By Jaikumar Vijayan Posted 2016-12-12 Print Google's Embedding Projector web applications will give developers a way to visualize how well their machine learning systems are interpreting data. Google has open sourced its Embedding Projector, a web application that gives developers a way to visualize data that's being used to train their machine learning systems. Embedding Projector is part of TensorFlow, the machine learning technology behind some popular Google services like image search, Smart Reply in Inbox and Google Translate. The company released TensorFlow to the open source community last year in order to spur more development activity in the field. In a technical paper, Google researchers described the Embedding Projector as an interactive visualization tool that developers can use to interpret machine-learning models that rely on what are known as "embeddings."

Detecting bird sounds in audio is an important task for automatic wildlife monitoring, as well as in citizen science and audio library management. The current generation of software tools require manual work from the user: to choose the algorithm, to set the settings, and to post-process the results. This is holding bioacoustics back in embracing its "big data" era: let's make this better! In collaboration with the IEEE Signal Processing Society we propose a research data challenge for you to create a robust and scalable bird detection algorithm. We offer new datasets collected in real live bioacoustics monitoring projects, and an objective, standardised evaluation framework – and prizes for the strongest submissions.

Technology is playing an ever-important role in the shopping side of the holiday season. Logistics aside, which is of course critical at this time of year, tech is also proving increasingly key from an experiential and a customer service perspective both online and offline. Leading that charge for 2016 are virtual reality (VR) and artificial intelligence (AI). Google has employed the former this year, for instance, to allow consumers to'walk' along Fifth Avenue in New York to experience all the holiday window displays. Window Wonderland, as the initiative is called, is a VR experience that lets users view 18 different retailers including Bloomingdale's, Barneys New York, Saks Fifth Avenue, Tiffany & Co, Burberry and more.

In the sci-fi film Ex Machina, reclusive inventor Nathan Bateman foresees a bleak future, telling the movie's protagonist Caleb that, "One day the AIs are going to look back on us the same way we look at fossil skeletons on the plains of Africa." When we don't understand something, we tend to fear it; which is one reason popular movies like Ex Machina and HBO's nail-biting new series Westworld like to imagine futures in which artificial intelligence plots to destroy humanity. Fortunately, AI is far more likely to recommend those titles to your Netflix queue than to result in a dystopian society out of a George Orwell novel. While technologies including Amazon's Alexa have been busy making people's lives outside of the workplace easier, bots were the big office story in 2016, helping companies handle routine tasks such as managing support tickets and streamlining workflows. In the coming years, machine learning will take on more of the non-routine work as well, ushering in the new era of artificial intelligence--one that looks to be far brighter than the future Hollywood typically envisions.

Sites selection for warehouses to minimize distribution costs (proximity to vendors, balanced against proximity to consumers). How many warehouses are needed, and what capacity each of them should have. Selection of optimal routes, schedules, and products groupings, to minimize delivery costs (using graph theory) Supply chain optimization (III). Minimize time spent by drivers in traffic jams (requires traffic prediction) while optimizing delivery speed, gas usage and other factors (better be stuck 20 minutes in a traffic jam than a costly detour, or departing later?) Pricing and profit optimization (per-product price elasticity studies needed; may require products to be aggregated in categories, to create buckets that yield statistical significance) Fraud detection for credit card transactions (use decision tree methods).

PayPal fights fraud with machine learning consuming more than 1.1 petabytes of data for 169 million customer accounts at any given moment. Airbnb uses Aerosolve Machine Learning package to help owners set a price for rental based on features of home, time of year, demand etc. Amazon's recommendation engine is one example where machine learning drives a lot of economic value Microsoft Azure Machine Learning, Google Prediction APIs, Amazon ML and IBM Watson Developer Services come with ready made algorithms that allow businesses to extract patterns from data, predict trends, identify language translations, understand social media sentiments, just to name a few. Apple Siri, Google Now, and Microsoft Cortana-like digital personal assistants are making use of Machine Learning for speech recognition to become smarter & creative therefore knowing more about you and your needs. By connecting the sensors and systems in each of their elevators to the cloud, ThyssenKrupp, a Garman Elevator Manufacturer, has been able to move beyond preventative maintenance to offer predictive and preemptive services, a service that has not been possible before in the elevator industry. Today after 56 years even the Barbie doll is going to become interactive and internet connected, that can talk to children and respond to their questions.