LOS ANGELES -- Looking back at 2017's tech products that bombed, none of these misses was more glaring than some wild video sunglasses that didn't exactly snap with consumers. The cute $129 video sunglasses called Spectacles from Snapchat parent Snap were initially hard to get. Then Snap put them on sale nationally in February and the broader based of national consumers showed little interest in the product, leaving a backlog of thousands of unsold glasses, and a $40 million write-down from Snap. A Wi-Fi enabled juicer with a sky-high price tag of $400 didn't make a lot of sense to consumers, especially when it required a subscription to buy proprietary Juicero bags of fruit to turn into juice. Another blow against this product was the discovery that squeezing the bags with your raw hands worked just as well as turning on the juicer.

Modern ethos is that all data is valuable, should be stored forever, and that machine learning will one day magically find the value of it. You've probably seen that EMC picture about how there will be 44 zettabytes of data by 2020? Remember how everyone had Fitbits and Jawbone Ups for about a minute? Now Jawbone is out of business. Have you considered this "all data is valuable" fad might be the corporate equivalent?

In the past few years the wearables sector has been a hive of activity, but it feels like this year things have accelerated, particularly in the realm of health and wellness. A report by Forrester Research projected that 29 percent of Americans will use wearable devices, compared with 18 percent in 2015. It predicts that wearables sales will rise from 4.2 billion in 2015 to 9.8 billion in 2021. There has been more interest in wearables companies doing clinical validation. The AARP has been using its Project Catalyst initiative to validate the usability of connected devices and activity trackers for seniors.

I used machine learning to predict Jawbone's fitness wearable device based on recorded smartphone sensors. Even basic Android phone has built-in sensors for tracking ambient temperature, device orientation, acceleration and many more. I made the tiny application to record sensor data and log it into a file. This app is a service continuously running in background and tracks my phone sensor's data. I also wear Jawbone UP fitness tracker in same time. It records my steps and activity.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

When we first began to explore the idea of building a step classifier, we knew we would be constrained to a very limited population of individuals (Jawbone employees) available to us for early development and testing. It seemed certain that the development of the classifier would be very iterative in that, as we tested larger and more varied sets of individuals and behaviors, we would undoubtedly find issues that we needed to quickly correct. So we would need a technical approach that was suited to rapid updates and that those updates would need to be essentially risk free. We could not afford the risk and development time of actually writing new code as we iterated. In short, we needed a step classifier that learned.