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.

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.

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.

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.

Alphabet, the technology giant formerly known as Google, still makes majority of its revenue from ads, but it's starting to build big gains elsewhere. SEE ALSO: Google's PR team penned the mother of all replies Google reported $3.7 billion in other revenue, including hardware and leaving out advertising, for the fourth quarter of 2016. It was bump up by 62 percent from the year prior. "Our growth in the fourth quarter was exceptional," said Ruth Porat, CFO of Alphabet, in a statement. "This performance was led by mobile search and YouTube.

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.

Want to implement machine learning algorithms from scratch? A recent KDnuggets poll asked "Which methods/algorithms you used in the past 12 months for an actual Data Science-related application?" with results found here. The results are analyzed by industry employment sector and region, but the main take away for the uninitiated is that there are a wide array of algorithms covered. And let's be clear: this is not a complete representation of available machine learning algorithms, but rather a subset of the most-used algorithms (as per our readers). There are lots of machine learning algorithms in existence today.

There was one big name missing from the Partnership on Artificial Intelligence (AI) member list when the research consortium was announced last September. Google, Facebook, Amazon, IBM, and Microsoft all pledged to work together to ensure AI is developed safely and ethically but Apple refused to get involved. Now it looks like the world's largest company and a tech giant renowned for keeping its research efforts a secret may have reconsidered its decision. Citing sources with knowledge of the situation, Bloomberg reported on Thursday that Apple is set to join the elite club, going on to say that its admission could be announced as early as this week. Apple has been gradually building up its AI and machine learning capabilities and buying a succession of small AI startups.