After we teach our kids how to survive with things like getting food into their mouths instead of all over their faces, using their words to communicate instead of pointing or grunting and wiping their own butts, we move on to social lessons. As we teach these lessons every day in the classroom or at the grocery store, it's inevitable that we slowly start to hear that faint echo of our parents' voices. It's as if their voices have been channeled through our own as we constantly remind our kids that it's not ok to burp without saying excuse me or to sneeze without covering their mouths. We hope they'll listen and learn because we know what happens to parents who have kids with bad manners. That judging glare we get at every event where parents and children are gathered in one place and we decide who the "bad" and "good" parents are simply by their obnoxious child's behavior.

In building automation, sensors such as motion detectors, photocells, temperature, and CO2 and smoke detectors are used primarily for energy savings and safety. Next-generation buildings, however, are intended to be significantly more intelligent, with the capability to analyze space utilization, monitor occupants' comfort, and generate business intelligence. To support such robust features, building-automation infrastructure requires considerably richer information that details what's happening across the building space. Since current sensing solutions are limited in their ability to address this need, a new generation of smart sensors (see figure below) is required to enhance the accuracy, reliability, flexibility, and granularity of the data they provide. Data Analytics at the Sensor Node In the new era of the Internet of Things (IoT), there arises the opportunity to introduce a new approach to building automation that decentralizes the architecture and pushes the analytics processing to the edge (the sensor unit) instead of the cloud or a central server.

Tapan Agarwal, Product Council Head at iGTB, has been cited in Global Trade Review in an article discussing the use of AI in the financial services industry. The article describes how financial services provides a fertile ground for AI applications because AI's strength comes from the quality of the data fed to it, and financial institutions themselves are data mines. In trade finance, AI applications can be found particularly in the field of compliance to prevent money laundering and fraud. Banks are currently facing the challenge of increased regulation in these areas, and keeping up with various requirements can be challenging for compliance departments. "Banks are failing to identify threats and fraudulent activities by relying solely on curated databases," commented Agarwal.

The chatbot lawyer that overturned hundreds and thousands of parking tickets is now tackling another problem: homelessness. London-born Stanford student Joshua Browder created DoNotPay initially to help people appeal against fines for unpaid parking tickets. Dubbed "the world's first robot lawyer", Browder later programmed it to deal with a wider range of legal issues, such as claiming for delayed flights and trains and payment protection insurance (PPI). Now, Browder, 19, wants his chatbot to provide free legal aid to people facing homelessness. He said: "I never could have imagined a parking ticket bot would appeal so much to people. Then I realised: this issue is bigger than a few parking tickets."

Apple CEO Tim Cook was the subject of an in-depth interview published over the weekend in The Washington Post in which he talked about a wide range of issues, including augmented reality (AR) and artificial intelligence (AI), the future of the iPhone, and the company's North Star. In the interview, Cook dismissed the idea of the iPhone accounting for two-thirds of Apple's revenues being problematic, calling the smartphone's dominance a privilege and expressing his belief that one day, every person on earth will own a smartphone. Cook also defended the company's progress in AI technology, pointing to the expanding capabilities of Siri, the digital assistant that Apple launched in 2011. Apple is opening up Siri to third-party developers so the technology can be used by other applications -- such as Uber or Lyft, as Cook pointed out -- to help users complete tasks faster and more efficiently. Earlier this month, the company reportedly bought Turi, a Seattle-based startup company and the latest purchase in a string of acquisitions aimed at bolstering its machine learning and AI capabilities.

Boltt is a new sports technology startup that wants to change the way athletes use wearables. Rather than just providing metrics like most fitness wearables, it wants to introduce usable guidance through artificial intelligence. In short, while the athletes focus on their training, an AI-enabled personal coach crunches the data and gives personalized instructions. Boltt is working on this technology in partnership with Garmin. To come up with customized and informed advice, it will track movements of the athlete and classify them according to time, type and intensity.

Research from Narrative Science claims confusion over the definition of artificial intelligence is holding it back, although 62% of enterprise respondents believe it will be place by 2018. Although this is an encouraging statistic, the report also highlights there is confusion over the definition of the technology itself (Check out Narrative Science's explanation of the different facets of AI at the bottom of the story). These statistics more than anything else highlight confusion, and ignorance to the artificial intelligence technology which is already present in their day-to-day lives. From Siri on Apple devices to Amazon's recommended purchases or Facebook's content recommendations, AI has been drip feed into the real-world of technology with few people realizing its impact. The functions mentioned are AI at one of its simplest versions, though IBM has been making progress with its Watson offering moving into more complex arenas, such as medical diagnosis, building management and weather modelling systems. But what is the real potential of artificial intelligence?

Most of us are fascinated by creativity. New ideas in science and art are often hugely exciting – and, paradoxically, sometimes seemingly "obvious" once they've arrived. But how can that be? Many people, perhaps most of us, think there's no hope of an answer. Creativity is deeply mysterious, indeed almost magical.

Deep-learning artificial intelligence has mostly relied upon the general-purpose GPU hardware used in many other computing tasks. But Intel's recent acquisition of the startup Nervana Systems will give the tech giant ownership of a specialized chip designed specifically for deep learning AI applications. That could give Intel a huge lead in the race to develop next-generation artificial intelligence capable of swiftly finding patterns in huge datasets and learning through imitation. Nervana has leaned heavily on GPU hardware to build its own portfolio of deep-learning AI services for both companies and independent developers. But the startup has also been developing its own specialized deep learning hardware, called Nervana Engine, that includes only the components necessary for running deep-learning algorithms and eliminates the extra components used for general-purpose GPU tasks.

Python is a general purpose and also a comparatively easy to learn programming language. Hence it is the language of choice for data scientists to prototype, visualize, and run data analyses on small and medium-sized data sets. This is a unique book that helps bridge the gap between machine learning and web development. It focuses on the difficulties of implementing predictive analytics in web applications. We focus on the Python language, frameworks, tools, and libraries, showing you how to build a machine learning system. You will explore the core machine learning concepts and then develop and deploy the data into a web application using the Django framework.