Classification is an important and central topic in ML, which has to do with training machines how to group together data by particular criteria. Classification is the process where computers group data together based on predetermined characteristics — this is called supervised learning. There is an unsupervised version of classification, called clustering where computers find shared characteristics by which to group data when categories are not specified.
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Of all the major league sports in the United States, basketball is the most balletic…in my humble opinion. Basketball demonstrates a fluidity of complex full-body motion all the while interactively and iteratively guiding and correcting the trajectory of a ball which repeatedly rebounds from the hard court surface. Watching real basketball players drive through the crowd toward the hoop gives us a hint of the artfulness. Watching it in slow motion makes us stare in wonder at the complexity of the performance.
Transitioning to the world of video games (where many sports seem to find their way) the observer has quite a different impression watching today’s state-of-the-art synthetic players. The rendered players go through the motions, but the simulation just doesn’t seem real. Even though the characters themselves look quite good in static poses there is something clearly counterfeit about how they move. No matter how great the skills of the animator are, it seems impossible to specify all of the angular velocities at all of the joints for even the most basic moves. It’s not that we search to find subtle flaws in their movement, but rather that we are instantly struck with how unnatural these players are.
With TensorFlow.js, you can not only run machine-learned models in the browser to perform inference, but you can also train them. In this super-simple tutorial, I’ll show you a basic "Hello World" example that will teach you the scaffolding to get you up and running.
Let’s start with the simplest Web Page imaginable:
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We know Grakn can be leveraged to model highly complex data, but how do we go about building a detailed model of a real-world system?
Here, we delve into Transport for London (TFL) data to understand and gain insights into the operation of the London Underground Network.
We go on to build surely the most desirable tool for such a network: a journey planner. (Because who doesn’t want to shave 0.3 minutes off their commute?)
https://ankaa-pmo.com/wp-content/uploads/2018/08/planning-logistics-and-operations-modeling-the-london-tube-network.jpg313500Service comm.https://ankaa-pmo.com/wp-content/uploads/2017/04/Logo-Ankaa-engineering.pngService comm.2018-08-23 16:02:022018-08-23 16:02:02Planning, Logistics, and Operations: Modeling the London Tube Network
An estimated 21 million connected vehicles are on the road today, gathering endless amounts of data. How does this data impact the way we do business and transform the world around us?
A few decades ago, personal vehicle and fleet navigation was completely dependent on paper road maps that no one ever knew how to fold! Digital maps are one of the many examples of how technology has completely revolutionized our world. Mapping the world on the internet rather than on paper has not only changed the way we navigate but also opened up various new business models like UBER and OLA, which we now take for granted.
Autonomous vehicles are here to stay with most manufacturers investing heavily in this direction to make their vehicles part of a connected world. Whether it is your own car or the truck that your business owns, all these are now becoming part of an expanding connected world.
With global digitalization, technologies are taking over all aspects of our lives. We see the automation of business processes and services, smart homes, and much more. However, what else we see is the gradual isolation of individuals from each other as they have their own small worlds, locked in their smart devices.
In the last few years, people have been investing in social media heavily. The rise of bloggers does not seem strange now, but we did not expect it to be a real job, say, 5 years ago. Today, most millennials dream of becoming a YouTuber or an Instagram blogger (and most of them succeed, to be honest).
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Back in November last year, Forrester posted an article with a stark warning: "AI hard-fact — treat it like a plug-and-play panacea and fail." The hype surrounding Artificial Intelligence (AI) has only grown since then.
Today, a cursory Google search of the term "AI" results in 2.4 billion entries. That’s a lot of AI chatter! While a Google search may be a blunt instrument for measuring the true impact of AI, it does illustrate just how "big" it has grown. If your inbox is anything like mine, I’m sure you’re only too aware that AI-everything is being used to solve AI-anything. That is exactly why Forrester’s prediction was so apt.
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The days of leaving Slack to create an event on your calendar are over!
In this tutorial, you are going to learn how to create a scheduler bot that adds events to your personal calendar with a simple Slack slash command using the Nylas Calendar API.
At Grakn, we recently released Grakn 1.3, with a slew of new features, bug fixes, and performance enhancements. Included in this release are new gRPC-based drivers for Java, NodeJS, and Python. This article will walk you through the Python driver and provide guidelines on how you can write your own for your language of choice.
Overview
The main reason for rewriting our drivers was a move from REST to gRPC in Grakn. This change has cleaned up our API and should provide performance benefits. Further, all of our available drivers (Java, Node, and Python) now expose the same objects and methods to users, subject to language naming conventions and available types. To maintain this uniformity across the stack, new language drivers should provide the same interface. Note that you will require both gRPC and protobuf support to create a functioning driver, so double check a) that compilers for your language exist, and b) your target language version is compatible with the compiler.
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According to Gartner, smart cities will be using about 1.39 billion connected cars, IoT sensors, and devices by 2020. The analysis of location and behavior patterns within cities will allow optimization of traffic, better planning decisions, and smarter advertising. For example, the analysis of GPS car data can allow cities to optimize traffic flows based on real-time traffic information. Telecom companies are using mobile phone location data to provide insights by identifying and predicting the location activity trends and patterns of a population in a large metropolitan area. The application of Machine Learning to geolocation data is being used in telecom, travel, marketing, and manufacturing to identify patterns and trends for services such as recommendations, anomaly detection, and fraud.
In this article, we discuss using Spark Structured Streaming in a data processing pipeline for cluster analysis on Uber event data to detect and visualize popular Uber locations.
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