Nine years and a few days ago, Apple released the iPhone, and the world was never the same. With the iPhone's decade mark in sight, the tech world is starting to get a little restless for the next big shift in computing, even as the iPhone itself starts to look a little boring. Smartphones have revolutionized everything from retail to banking to education. Depending on who you ask, the next big thing will be robots, or artificial intelligence, or virtual reality, or health-tracking devices, or self-driving cars, or any number of other trends. The bad news is that we probably won't another big, world-shaking product introduction like the iPhone any time soon.

A 2014 Gallup Poll puts a positive spin on the rise of Americans' satisfaction with our education system stating that 48% of Americans are satisfied. This is not something to be celebrated. Our education system needs an extensive overhaul. The standards put on all students do not measure a student's true knowledge. The type of testing mandated does not test to students' abilities but rather how well they know how to take a test. Teachers are leaving the profession in droves due to the immense work load, low pay, lack of support and insurmountable requirements to advance student performance.

Artificial intelligence is making rapid progress and has the potential to bring huge benefits to many aspects of our lives, including improved services for sick, elderly or disabled people, disease diagnosis, development of new medicines, perfecting driverless vehicles and resolving problems related to climate change. While AI is expected to contribute to enhancing people's well-being and happiness, it has the potential of doing harm or being used for unethical purposes. The government, scientists, engineers and businesspeople involved in applying AI should push discussions on such questions as the relationship between AI and people and society, and ethical issues involved in the use of AI. Particularly important will be to deepen discussions on how to prevent harm caused by the wrongful use of AI. Such discussions will be indispensable to building public trust in AI and robots.

A great trainer makes working out 10x more motivating, fun, and effective. That's why we created Vi--an evolving personal trainer who lives in bio-sensing earphones. Put Vi on and start a relationship with a friend for your fitness. Each day, Vi tracks you, gets smarter, and coaches you to real results. Vi will help you meet your weight goals, improve your running, cycling and training.

Consumer interests have changed, and they will keep changing. Social media switched the balance of power away from company-controlled communication and into the consumer camp -- not just in-the-moment, but precisely at their moment, on their devices, on the channels they choose. But with the advent of messaging and the recent injection of A.I. into messaging platforms in the form of chatbots, it's more than just communication that is shifting -- it's the entire way people and businesses interact. While messaging platforms are the future of consumer-brand engagement, A.I. technology is in its infancy as it relates to truly engaging with humans. There is a long way to go before people are having open-ended conversations with chatbots that don't end in disappointment. Before I explain my point, here's some background on messaging apps.

It was a week marked by some innovative hacks in the security world. After a slew of hi-profile Twitter account takeovers, we talked to OurMine, the group responsible. Researchers found a way to glean data from an air-gapped laptop by modulating the fan. And we took a look at why two-factor authentication using text messages doesn't add as much protection as you think. Symantec turns out to be riddled with vulnerabilities, as is often the case with antivirus software.

A group of researchers from Nanyang Technological University, Singapore, have created a novel solution for large-scale Android malware detection. It's called DroidOL, and it's an adaptive and scalable malware detection framework based on online learning. "DroidOL's achieves superior accuracy through extracting high quality features from inter-procedural control-flow graphs (ICFGs) of apps, which are known to be robust against evasion and obfuscation techniques adopted by malware," the researchers explained. They used the Weisfeiler-Lehman (WL) graph kernel to extract semantic features from ICFGs, and finally, online learning to distinguish between benign and malicious apps. They attribute much of the success of their technique to the use of a scalable online learning classifier instead of batch-learning classifiers (which are not).

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Companies like Google, AWS and Microsoft have introduced their own machine learning platform in cloud. In this blog, we are going to share my experience while getting started with machine learning platform. Very first question that I ask myself was "Why this provider?"

Does anyone have the in-video quizzes or quizzes with solutions and explanations/feedback?