This chapter intends to introduce the main objects and concepts in TensorFlow. We also introduce how to access the data for the rest of the book and provide additional resources for learning about TensorFlow. After we have established the basic objects and methods in TensorFlow, we now want to establish the components that make up TensorFlow algorithms. We start by introducing computational graphs, and then move to loss functions and back propagation. We end with creating a simple classifier and then show an example of evaluating regression and classification algorithms.

Want to use artificial intelligence (AI) to train autonomous drones and self-driving cars the way Microsoft does? Microsoft recently shared its open source system for performing such trainings, including its AI-based platform AirSim. The project, called the Aerial Informatics and Robotics Platform, aims to provide simulation tools and resources to help in the training of robots, drones, and other hardware that can operate autonomously, according to a Microsoft blog post. By improving the way that these devices and vehicles can navigate their environment, the hope is that that can eventually shed their "emerging technology" title. By using virtual simulators, like AirSim, a team is able to collect valuable data and train the systems more efficiently before they take them out of the lab.

FinovateEurope 2017: what awaits in the post-bank world? Last week's FinovateEurope was full of the broad range of exciting and innovative companies that we have come to expect at the show. Although the 72 companies showcasing their solutions operated right across the fintech landscape, they all sing from a common hymn sheet of improving the customer experience. Rapidly advancing applications of artificial intelligence (AI), conversational user interfaces and other emerging technologies are driving a shift away from the conventional financial services approach to a more engaging and personalised customer journey. What that actually means and how that is done was explored in a workshop run by R/GA London at the conference.

Last winter, as meteorologists warned of a monster El Niño, researchers at the Nature Conservancy in California prepared to mobilize. El Niño promised to bring in king tides that would raise the sea level by as much as one foot above normal during high tide, causing flooding along the coastline that researchers could study as a preview of climate change-induced sea level rise. But when a king tide arrives, it floods lots of pockets along the coastline at once. So they decided to try a new, distributed surveillance strategy: commercial drones, co-opted from a gung-ho statewide network of citizen scientists. The plan had a lot of advantages.

Personetics, which has offices in New York, London and Tel Aviv, creates "chatbots" that can respond to customer questions through popular messaging platforms like Facebook Inc's Messenger. French bank Societe Generale, for instance, is using Personetics to answer queries about equity funds in its Romanian banking unit.

Facebook is secretly building artificially intelligent systems that can read people's private messages, according to a major manifesto published by Mark Zuckerberg. The Facebook CEO posted a 6,000-word essay in which he worried about the end of globalisation and seemed to suggest that Facebook would be necessary for the future safety of the world. But it didn't include one paragraph that had been expected to be part of it – apparently as a result of the worrying features it suggested the social network was developing. In one version of the text, Mr Zuckerberg wrote about the fact that Facebook appeared to be using artificial intelligence for online surveillance. It could eventually develop robots that would read through people's private messages and check for anything that it deems worrying, the manifesto read. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

From the millions of Amazon Alexas to the self-driving car, new products are coming to market infused with machine learning. The innovation offered by machine learning techniques are real, and they will changed the SaaS world. How can startups use machine learning to their advantage? These applications alone make for tremendous advances. But, combinations of these applications lead to incredible things.

Paraphrasing Garrison Keillor, it's been a quiet week in the Apache Spark community - at least compared to last year, where the definitive Spark 2.0 was unveiled. Last week, Spark Summit pulled into Boston, and so did one of those nor'easters that make Boston so alluring in February. And so the Spark project, for now, is engaging in the blocking and tackling chores of cleaning up or optimizing APIs. For instance, a recent update of Spark 2.0 has added pipeline processing for enabling more efficient running of complex machine learning jobs. And of course while we're on that topic of machine learning, it was virtually impossible to evade presentations covering it.

Depictions of the future in books and film are usually influenced by what's going on at the time, reflecting social malaise, impending armageddon, or economic anxieties. The robots in classic sci-fi usually resembled humans, as most authors assumed they would eventually assist us in the same tasks humans did. Instead, however, today we find artificial intelligence doing some of our thinking for us, but it's more often solving problems that don't need intervention from self-contained humanoid robots. The promise of autonomous robots that matched our abilities has given way to a more specific focus on tasks that are fulfilled by armies of smaller bots controlled by machine learning and algorithms running in the cloud. Their scope is more complex, but a lot less dramatic than, say, Forbidden Planet's Robby the Robot, or the replicants from Blade Runner.

So I started this as a journey to learn how to get an Android App to tell me the brand of the beer I was having, which would be a cool base idea to develop an app such as "Vivino" that does precisely this but with wine. Wouldn't it be nice to have app such as "Vivino" but for beer?… I first started checking how could software determine the contents of an image and came across TensorFlow which is Google's answer to that question (and many many others). TensorFlow provides the base machine learning algorithms that Google use to determine the contents of your images, mails, and your life in general (obviously). Since it is open source you are able to extend it as you like; for example to re-train it with another specific images, improving its chances to classify them.