BOULDER, Colo.--(BUSINESS WIRE)--Few technologies have the transformative potential to reshape how we live, move, and work. Electricity and the Internet were two technologies that fundamentally transformed life in the 20th century. Artificial intelligence (AI) is the 21st century equivalent of electricity and the Internet. According to a new white paper from Tractica, AI is expected to bring massive shifts in how people perceive and interact with technology, with machines performing a wider range of tasks, in many cases doing a better job than humans. Tractica's white paper analyzes 10 key trends that are influencing the development of the global artificial intelligence market, and is available for free download on the firm's website.

Apple's iPhones and iPads are losing the battle against Android devices. That's according to a new study by mobile diagnostics firms Blancco Technology Group (BTG), which claims that Apple's devices are less reliable and experienced a bigger failure rate than their Android counterpart, driven by bugs in the iOS 10 update. For the purposes of the report the word "failure" refers to any number of problem including instances of apps crashing, connection difficulties and overheating. About 62 per cent of iOS devices suffered performance failures in the third quarter of 2016 compared with 47 per cent of Android devices, the report found. The iPhone 6 was the main culprit with the highest failure rate of 13 per cent.

We now experience life through an algorithmic lens. Whether we realize it or not, machine learning algorithms shape how we behave, engage, interact, and transact with each other and with the world around us. Deep learning is the next advance in machine learning. While machine learning has traditionally been applied to textual data, deep learning goes beyond that to find meaningful patterns within streaming media and other complex content types, including video, voice, music, images, and sensor data. Deep learning enables your smartphone's voice-activated virtual assistant to understand spoken intentions.

As continuous big data processing is gaining popularity it naturally implies that there is a need to transition many of the distributed machine learning functionality to a streaming backend. The most common use case is to give streaming predictions based on the model learnt in batch, however in some cases it is beneficial to also update the model on the fly. It is not uncommon that streaming learners need different algorithms than their batch counterparts. It also offer a dive into the implementation of a Scala library augmenting FlinkML with streaming predictors.

Uber is rolling out the biggest changes yet to its mobile app since 2012, with machine learning driven predictions behind the redesign. The app redesign has focused on greater personalisation, and that requires some extensive machine learning work on the back end. Unlike most mobile app companies, Uber doesn't measure success based on engagement levels but rather how quickly you can get through the booking process. The new app starts by asking for your destination, including a number of predictions based on your habits and your current location. For example if you are at the office it will assume you want to go home, or if it is'Thirsty Thursday', your favourite pub. You can also integrate your calendar with the app so it knows when and where your meetings and appointments are.

Today's developers often hear about leveraging machine learning algorithms in order to build more intelligent applications, but many don't know where to start. One of the most important aspects of developing smart applications is to understand the underlying machine learning models, even if you aren't the person building them. Whether you are integrating a recommendation system into your app or building a chat bot, this guide will help you get started in understanding the basics of machine learning. This introduction to machine learning and list of resources is adapted from my October 2016 talk at ACT-W, a women's tech conference. While this is only a brief definition, machine learning means we can use statistical models and probabilistic algorithms to answer questions so we can make informative decisions based on our data.

The neural-redis module provides an easy way to simulate a Multi-layer Neural Network that can do regression and classification, and it is designed to be native supported by redis server. The output 13 means the number of tunable parameters in the neural network. Part 2 will show you how to train the neural network.

Lili Cheng, a Distinguished Engineer at the Artificial Intelligence and Research Group has taken to the Microsoft Azure Blog to announce the new Azure Bot Service. The announcement comes after the launch of the Bot Framework on Github in March, and marks a way for Microsoft to make it easier for software developers to get started creating a bot. The Azure Bot Service will become the first public cloud bot-service powered by the Microsoft Bot Framework and serverless compute in Microsoft Azure. According to Lili Cheng, "With this cloud service, you can build, connect, deploy and manage intelligent bots that interact naturally wherever your users are talking." Bots will also scale based on demand, meaning you will only pay for the resources your bots consume.

Editors at the MIT Technology Review recently weighed in with their annual review of the world's 50 Smartest Companies. This list celebrates the most effective pairing of innovation and business across the globe. For the first time, more than 20% of MIT's picks rely on artificial intelligence to support their business at a fundamental level, somewhat redefining what it means to be a truly "smart" company today. It's working on speech recognition intelligence called Deep Speech 2. This reduces the chance of accidents on autopilot by 50% relative to the safety record of human drivers, according to CEO Elon Musk. Now, Tesla automobiles come off the assembly line "future ready" for complete self-driving.

Adobe held its annual MAX conference for users of its Creative Cloud earlier this month. That's where the company usually announces new and upcoming features to applications like Photoshop or Premiere Pro. This year, however, Adobe also introduced Sensei, its new artificial intelligence- and machine learning-based platform that combines Adobe's knowledge of working with photos, videos, documents and marketing data with a unified AI and machine learning framework. Just like Microsoft and Google are trying to imbue all of their products with "intelligence," Adobe, too, is now on a mission to bring more smarts to its products -- be that in the form of machine learning-based tools and features, or through smarter traditional analytics. Sensei is Adobe's version of this.