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

Like the large-vocabulary speech recognition paper we looked at yesterday, today's paper has also been described as a landmark paper in the history of deep learning. The ImageNet dataset contains over 1.5 million labeled high-resolution images of objects in roughly 22,000 categories. The annual ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) competition uses a subset of ImageNet with roughly 1000 images in each of 1000 categories. There are 1.2M training images, 50,000 validation images, and 150,000 testing images. For reporting error rates, a model predicts the top 5 most likely labels.

In 2025, in accordance with Moore's Law, we'll see an acceleration in the rate of change as we move closer to a world of true abundance. Here are eight areas where we'll see extraordinary transformation in the next decade: In 2025, 1,000 should buy you a computer able to calculate at 10 16 cycles per second (10,000 trillion cycles per second), the equivalent processing speed of the human brain. The Internet of Everything describes the networked connections between devices, people, processes and data. By 2025, the IoE will exceed 100 billion connected devices, each with a dozen or more sensors collecting data. This will lead to a trillion-sensor economy driving a data revolution beyond our imagination. Cisco's recent report estimates the IoE will generate 19 trillion of newly created value. With a trillion sensors gathering data everywhere (autonomous cars, satellite systems, drones, wearables, cameras), you'll be able to know anything you want, anytime, anywhere, and query that data for answers and insights. SpaceX, Google (Project Loon), Qualcomm and Virgin (OneWeb) are planning to provide global connectivity to every human on Earth at speeds exceeding one megabit per second. We will grow from three to eight billion connected humans, adding five billion new consumers into the global economy. They represent tens of trillions of new dollars flowing into the global economy. And they are not coming online like we did 20 years ago with a 9600 modem on AOL. Existing healthcare institutions will be crushed as new business models with better and more efficient care emerge. Thousands of startups, as well as today's data giants (Google, Apple, Microsoft, SAP, IBM, etc.) will all enter this lucrative 3.8 trillion healthcare industry with new business models that dematerialize, demonetize and democratize today's bureaucratic and inefficient system. Biometric sensing (wearables) and AI will make each of us the CEOs of our own health. Large-scale genomic sequencing and machine learning will allow us to understand the root cause of cancer, heart disease and neurodegenerative disease and what to do about it. Robotic surgeons can carry out an autonomous surgical procedure perfectly (every time) for pennies on the dollar. Each of us will be able to regrow a heart, liver, lung or kidney when we need it, instead of waiting for the donor to die. Billions of dollars invested by Facebook (Oculus), Google (Magic Leap), Microsoft (Hololens), Sony, Qualcomm, HTC and others will lead to a new generation of displays and user interfaces.

Infrared cameras that track eye movements could detect concussions in less than a minute, offering insight into whether athletes or children have sustained the injury. The device, called'Eye-Sync' has now been approved by the US Food and Drug Administration and has been developed amid growing concerns over brain injuries in contact sports. Head trauma affects the brain's anticipatory neural network and Eye-Sync focuses on analysing visual response in this network. Infrared cameras that track eye movements could detect concussions in less than a minute, offering insight into whether athletes or children have sustained the injury. The device, called'Eye-Sync' (pictured) has now been approved by the US Food and Drug Administration The device was developed by Boston-based SyncThink. A user puts on a virtual reality headset connected to a computer tablet, with a moving circle appearing in the display.

A team of researchers at Ohio State University pinpointed the area of the brain that recognizes human facial expression. Their recent study reveals that this region - the posterior superior temporal sulcus (pSTS) - is on the right side of the brain behind the ear. The study utilized functional magnetic resonance imaging (fMRI) to identify the pSTS as the region that is activated when their test subjects viewed images of people making various kinds of facial expressions. Using the subjects' fMRI images and comparing them to facial muscle movements in test photographs, the team created a map of pSTS regions that activate for specific facial muscle groups In addition, the study revealed that particular neural patterns within the pSTS are used for specific facial movements, such as a furrowed brow. "That suggests that our brains decode facial expressions by adding up sets of key muscle movements in the face of the person we are looking at," said Aleix Martinez, a professor of electrical and computer engineering at Ohio State and senior author of the study.

Many people have gotten jobs in machine learning just by completing that MOOC. There're other similar online courses that help; for example the John Hopkins Data Science specialization. Participating in Kaggle or other online machine learning competitions has also helped people gain experience. Kaggle has a community with online discussions from which you can learn practical skills. Attending local meetups or academic conferences (if you can afford it) and talking to more experienced people will also help.

Artificial Intelligence (AI) is a growing area, and for good reason. But where AI lacks, humans excel. Humans, sometimes to a fault, are great at collaborating and checking in on nearly everything to make sure it works. So doesn't it make sense for the humans and AI to work side by side? This is just what DigitalGenius is doing to provide top-notch customer service for you.

This post contains my answers from a Quora session I did on machine learning and artificial intelligence. Each section contains a link to the original Quora question, the overall session can be found here. Think carefully about what you actually want to achieve with it. Most fall into the latter camp, but it seems everyone fancies themselves as containing a bit of the former (particularly if they think they're going to solve AI). To do the former well, in the international community, requires really good foundations (particularly in mathematics) followed by a PhD with a supervisor who has experience of how that community works. Doing the second well is much easier from the perspective of learning machine learning. A data generator would often be a scientist or company that is working in a particular application and wants answers. They need access to machine learning researchers or statisticians to give advice on how to answer those questions. They should try and collaborate with experts in data analytics and data science, but they should be careful, there is a lot of hype around the term'big data' at the moment. It's a difficult area to navigate. Data generators typically need an interface to consume machine learning (or statistics) effectively, if this interface is poorly chosen a lot of wasted resource can result (things get very expensive very quickly for a lot of data generators!). A data consumer is where the largest demand is right at the moment, and should probably be the starting point for someone who wants to move in the right direction. An MSc in Data Science would be a good starting point. You can also use this experience to see if you want to transit into a machine learning generator (that's basically what happened to me). What are you passionate about? That is the route in to any subject. Is it a particular approach to learning or a particular application?

Let's take a look at the steps in a machine learning workflow. You might have data in many places, such as multiple spreadsheets and databases. MATLAB provides interactive tools that make it easy to perform a variety of machine learning tasks, including connecting to and importing data. Apps can generate MATLAB code, enabling you to automate tasks. Oftentimes data has missing or incorrect values.

Cluster assessment is a staple of unsupervised machine learning and knowledge science. It is incredibly useful for knowledge mining and significant knowledge because it routinely finds patterns in the knowledge, without the will need for labels, contrary to supervised machine learning. In a true-world setting, you can think about that a robotic or an synthetic intelligence will not normally have access to the exceptional response, or it's possible there isn't an exceptional right response. You'd want that robotic to be able to investigate the world on its own, and study factors just by hunting for patterns. Do you ever ponder how we get the knowledge that we use in our supervised machine learning algorithms?