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.

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.

This story was delivered to BI Intelligence IoT Briefing subscribers. To learn more and subscribe, please click here. Nest is looking to improve the artificial intelligence (AI) and use of machine learning for its products, as indicated by the recent appointment of Yoky Matsuoka as the company's chief technology officer. Matsuoka, who previously cofounded Alphabet's X unit before becoming the VP of technology at Nest, will leave her current position at Apple to employ her expertise in AI and robotics for the company, according to Recode. This move will likely lead to Nest introducing the Google Assistant to its devices, better positioning the voice assistant within the competitive smart home market.

Rapid, radical advances in artificial intelligence and deep learning in business are changing the stakes--and ushering in a new era of business and technology. Artificial intelligence (AI) has always seemed a bit futuristic. The ability of machines to tap massive amounts of data and use algorithms to "think" and'adapt" on the fly has been an alluring, if t somewhat elusive, concept. However, over the past few years, advances in digital technology have begun to redefine the AI landscape and usher in real-world capabilities. "We are seeing the technology take off in a big way," says Satya Ramaswamy, vice president and global head of digital enterprise for Tata Consultancy Services (TCS).

Even though the phrase "image recognition technologies" conjures visions of high-tech surveillance, these tools may soon be used in medicine more than in spycraft. A team of Stanford researchers trained a computer to identify images of skin cancer moles and lesions as accurately as a dermatologist, according to a new paper published in the journal Nature. In the future, this new research suggests, a simple cell phone app may help patients diagnose a skin cancer -- the most common of all cancers in the United States -- for themselves. "Our objective is to bring the expertise of top-level dermatologists to places where the dermatologist is not available," said Sebastian Thrun, senior author of the new study, founder of research and development lab Google X and an adjunct professor at Stanford University. He added that those who live in developing countries do not have the same level of care as can be found in the US and other industrialized nations.