Robots Are Ready to Shake (and Stir) Up Bars WSJD - Technology

The silver-and-turquoise lounge, in the Miracle Mile Shops mall on the Strip, has 28 counter-style seats, each equipped with a tablet, facing a bar counter topped with two industrial-grade robotic arms. Patrons can order signature and classic cocktails, or fill a virtual cup with up to 14 ingredients of their choosing. Then the robotic arms go to work, gathering ingredients from a kind of futuristic back-bar automat; reaching up to a lattice of 120 liquor bottles; and tipping the resulting cocktail into a plastic cup proffered by a mechanical dispenser in the counter. Drinks take 60 to 90 seconds to make, and cost $12 to $16, said Stephan Mornet, president of Robotic Innovations, Tipsy Robot's parent company. For its automated bar, Tipsy Robot turned to Makr Shakr, an Italian startup that built its first robot bartender for Google I/O, the annual developer conference, in 2013.

AI (Reinforcement learning) Driven Back testing- RLBT


Traditionally, Back testing is a data based approach to decision making. Backtesting offers Research analysts, traders, and investors a way to evaluate and optimize their trading strategies and model portfolios before implementing them. This is done by using historical data, backtest the model to see whether it would have worked in the past. By comparing the predicted results of the model against the actual historical results, backtesting can determine whether the model has predictive value. This is where we, in essence, put your trading strategies and model portfolios into a time machine (i.e.

Facebook insists it's not raiding academia through its research partnerships with top universities


Facebook has in recent months amped up a push to bring in professors from top-notch universities to work on long-term artificial intelligence research part time, but the company says it views universities as partners rather than competitors to poach top talent from. In an interview with reporters, Chief AI Scientist of Facebook AI Research Yann LeCun gave new details about this "dual affiliation" program that lets professors do research for both Facebook and their universities. The program, most recently expanded to the University of Washington, Carnegie Mellon University in Pittsburgh and Oxford University in the U.K., has come under fire for fear of "brain drain" among top research institutions. That is far from the case argues LeCun, who pointed out that Facebook only brings in one or two people from each school so as not to disrupt the work of their departments. "We are careful; we don't hire five people from the same university," LeCun said.

The Singularity Known for Sofia the Robot is Coming: Past-ICO Review


The Skynet Funding Bill is passed. The system goes online Aug. 4th, 1997. Human decisions are removed from strategic defense. Skynet begins to learn at a geometric rate. It becomes self-aware at 2:14 a.m.

7 Machine Learning Algorithms To Start Learning.... MarkTechPost


It is a simple algorithm which can be used as a performance baseline. This algorithm methodology is used mostly for forecasting and finding out cause and effect relationship between data variables. Its purpose from a database is to read the data points which are separated into several classes and then predict the new sample point classification. It gives great results when used for textual data analysis. It is an unsupervised learning used in unlabelled data sources.

How Fortnite approaches analytics, cloud to analyze petabytes of game data


Fortnite processes 92 million events a minute and sees its data grow 2 petabytes a month. And when you have the most popular game in the world, you need an analytics architecture to match. Chris Dyl, director at platform at Epic Games, outlined the company's analytics architecture and how it has built its system on Amazon Web Services. What serverless architecture actually means, and where servers enter the picture Business analytics: The essentials of data-driven decision-making What is machine learning? Dyl, speaking at the AWS Summit in New York, outlined how Epic has moved to be all-in on AWS as well as extend usage via machine learning tools such as Amazon SageMaker, which has a bevy of built-in algorithms for developers to use.

Machine Learning, Data Science, and Statistics


There are no universally agreed-upon definitions for the terms "machine learning", "data science", and "statistics". In my mind, classical statistics consists of traditional techniques that were developed from the 1920s through the 1970s. Statistics techniques include things like correlation, linear regression, and the t-test for hypothesis testing. In my mind, machine learning consists of techniques that make predictions based on data and usually require computer analysis. Examples include logistic regression classification, neural network classification, and k-means clustering.

Notes from Microsoft Machine Learning and Data Science Summit – Day 1 – R&D


Microsoft Machine Learning & Data Science Summit is taking place in conjunction with Microsoft Ignite at Georgia World Congress Center. Today, day 1 started with keynote by Dr. Joseph Sirosh who identified three axes of innovation along with various customer case studies. Thought leaders and Microsoft engineers discuss the latest Big Data, Machine Learning, Artificial Intelligence, and Open Source techniques and technologies along with important case studies. There were various great take aways from sessions.

Thia Kai Xin's answer to Why is Python so popular in machine learning? - Quora


It is not the fastest. Scala, Julia is faster and as Riyad Parvez mentioned, most of the heavy lifting in Python are actually done by C or Fortran libraries backend. Neither is it the easiest to learn. R is easier for beginners. Rather, it is a general language that does a little of everything at a good enough complexity-performance tradeoff with a full suite of tools for productionizing machine learning.

How Apple will use AirPods and data science to create the world's most powerful bot


Apple's virtual assistant Siri is about to be the most well-fed data science algorithm on the planet. Over the past year, Siri hasn't looked good compared to her peers in the bot world -- Facebook and Amazon have opened up their bot platforms to developers, while Siri has continued to be handcuffed to a handful of limited use cases. But with the announcement of Apple's new wireless earbuds, called AirPods, earlier this month, it looks like Siri is about to be exposed to the daily activities of Apple users all over the globe. Apple's dual hardware/software AirPod launch will, if the smartbuds become as ubiquitous as the iPhone, allow Siri to jump from novelty to the preeminent A.I. bot platform in the world. Since Apple clearly intends for users to leave their buds in throughout all of their daily activities, from work to commuting to travel to exercise, Siri will be able to make suggestions at any point based on geolocation, a particular iPhone activity, a Bluetooth pairing (i.e. with a car or entertainment system), and more.