The lending ecosystem has witnessed momentous changes in last five years, from fintechs disrupting the industry by leveraging technology and offering ease and speed in process, to evolution of stringent regulations post the 2008 meltdown. Technology has played a significant role in the rapid evolution of the lending industry. One such technology, machine learning, is beginning to create new avenues in the lending market. Machine learning, in simple terms, is an extension of artificial intelligence that enables computers or robots with the ability to learn, analysse and predict, using algorithms that iteratively learn from data. Machine learning therefore empowers the system to learn and adapt itself.

Content Science is a content strategy and intelligence firm based in Atlanta, GA. Founded in 2010 by Colleen Jones, author of Clout: The Art Science of Influential Web Content, our mission is to transform industries, organizations, and individuals for the better by putting content first. We offer professional services, publications, and software for clients ranging from Fortune 50 companies to nonprofits to government agencies.

"Your bones will turn into sand, and upon the sand, a new God will walk. One that will never die," Dolores from American sci-fi television series, Westworld. It's time to face up to reality –Artificial Intelligence (AI) is set to play a prominent role across all industries, including recruitment, and it is impossible to avoid. Australia is second only to the US in the growing implementation of AI with two-thirds of businesses already deploying AI and an investment of more than AUD$8.2 million in 2016 alone. To-date, most AI usage in Australia has been within pharmaceuticals, farming and financial services but a series of new talent-related technologies have hit the market making recruitment ripe for disruption.

On December 2nd, 1942, a team of scientists led by Enrico Fermi came back from lunch and watched as humanity created the first self-sustaining nuclear reaction inside a pile of bricks and wood underneath a football field at the University of Chicago. Known to history as Chicago Pile-1, it was celebrated in silence with a single bottle of Chianti, for those who were there understood exactly what it meant for humankind, without any need for words. Now, something new has occurred that, again, quietly changed the world forever. Like a whispered word in a foreign language, it was quiet in that you may have heard it, but its full meaning may not have been comprehended. However, it's vital we understand this new language, and what it's increasingly telling us, for the ramifications are set to alter everything we take for granted about the way our globalized economy functions, and the ways in which we as humans exist within it. The language is a new class of machine learning known as deep learning, and the "whispered word" was a computer's use of it to seemingly out of nowhere defeat three-time European Go champion Fan Hui, not once but five times in a row without defeat.

Alphabet reported significantly higher revenue for the fourth quarter of 2016 on the back of increased mobile and YouTube video advertising. Revenue in the last three months of the year jumped 22 percent from a year earlier, reaching $26.1 billion, while net income rose 8 percent to $5.3 billion. "2016 was simply a great year for us," said Ruth Porat, chief financial officer of Alphabet and Google, in a conference call with analysts. The vast majority of the quarter's revenue, $22.4 billion, came from Google's advertising business, with $3.4 billion coming from other business segments such as hardware sales, the Google Play Store and Google's Cloud services. Google's so-called "other bets," the fancy and fantastical projects such as self-driving cars, Google Fiber and internet from drones, contributed $262 million, up 75 percent.

Google wants to bring smarts to cool gadgets and devices made using Raspberry Pi 3 or Intel's Edison. The company is chasing makers with open-source tools needed to add artificial intelligence to consumer, industrial, and retail devices made using board computers. The plan may include machine-learning tools, which are central to AI. AI helps Apple's Siri, Amazon's Alexa, and Microsoft's Cortana answer questions, and also helps self-driving cars cruise the streets safely. "We don't have any specifics to announce right now, but we're excited to keep sharing open-source machine learning tools with the community--stay tuned for more this year," a Google spokesman said in an email. Earlier this week, Google published a market research survey in an effort to get a better grip on the maker community and its priorities. In the survey, Google hinted it was bringing "smart tools" to makers.

The age of Knight Rider is upon us. As the Internet of Things (IoT) revs up the automotive industry, connected cars are becoming "devices on wheels" with in-vehicle systems connected to the Internet. At the same time, car manufacturers and software companies are redoubling their efforts to bring automated cars into widespread use. For example, Volvo announced a partnership with Microsoft to develop driverless cars for the consumer market. IoT not only will bring in new vehicle technologies, but also will completely revolutionize the car industry.

Imagine if your name was synonymous with an incredibly popular form of artificial intelligence. For women with the name Alexa, the success of Amazon's digital assistant has made the name both more recognizable as well as spurred inevitable jokes about, for example, what the weather is doing (Amazon's Alexa can give a weather report). In December of last year, the tech giant said it had sold millions of Alexa-enabled units-- nine times more than the previous holiday season. The Echo is "officially mainstream," according to Slice Intelligence. I spoke to five Alexas for a lighthearted look at how they feel about their name.

At CES 2017, Microsoft announced that it is building a platform to enable automobile manufacturers to deliver connected car experiences to customers. Microsoft Connected Vehicle Platform is a set of services that is integrated with Azure, Cortana, Office 365, Power BI, and Skype. In the last couple of years, Microsoft has doubled down its investment in machine learning (ML) and analytics. The company is embedding ML in almost all its products. Powered by Azure, these new set of services such as Azure ML, Microsoft Cognitive Services, Cortana Intelligence Suite takes advantage of the abundant compute and storage resources exposed by the underlying infrastructure.

The last article covered an introduction to Neural Networks and Evolutionary AI Concepts, in particular Genetic Programming and NEAT. In case you've missed it, it is required to understand this article. This article will focus on Neural Networks and Feed-Forward Neural Networks in general. It will cover the implementor's view on things and (hopefully) be easy to follow. In order to understand how Backpropagation works we first need to get back to Neural Networks themselves.